This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training ...This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training set are optimally divided into several non-overlapping groups to maximize the label distinguishability in every group. Multiple classifiers are trained separately and ensembled for label predictions. Experimental results show significant improvement over previous weak label learning algorithms.展开更多
In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using...In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.展开更多
In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the researc...In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.展开更多
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f...Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.展开更多
Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and...Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.展开更多
It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical informati...It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.展开更多
The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral ...The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.展开更多
We demonstrate the feasibility of simultancous multi-probe detection for an optcal-resolution photoacoustic microscopy(OR-PAM)system.OR-P AM has elicited the attention of biomedical imaging researchers because of its ...We demonstrate the feasibility of simultancous multi-probe detection for an optcal-resolution photoacoustic microscopy(OR-PAM)system.OR-P AM has elicited the attention of biomedical imaging researchers because of its optical absorption contrast and high spatial resolution with great imaging depth.OR-PAM allows label-free and noninvasive imaging by maximizing the optical absorption of endogenous biomolecules.However,given the inadequate absoption of some biomolcules,detection sensitivity at the same incident intensity requires improvement.In this study,a modulated continuous wave with power density less than 3mW/cm^(2)(1/4 of the ANSI safety limit)excited the weak photoacoustic(PA)signals of biological cells.A microcavity traneducer is developed based on the bulk modulus of gas five orders of magnitude lower than that of solid;air pressure variation is inversely proportional to cavity volume at the same temperature increase.Considering that a PA wave expands in various directions,detecting PA signals from different positions and adding them together can increase detection sensitivity and signal-to-noise ratio.Therefore,we employ four detectors to acquire tiny PA signals simul-taneously.Experimental results show that the developed OR-PAM system allows the label-free imaging of cells with weak optical absorption.展开更多
In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different ...In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels.The paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation criteria.The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods.展开更多
Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of hi...Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.展开更多
In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most po...In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.展开更多
Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label c...Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.展开更多
A new method for multi-protocol label switching is presented in this study, whose core idea is to construct model for simulating process of accommodating network online loads and then adopt genetic algorithm to optimi...A new method for multi-protocol label switching is presented in this study, whose core idea is to construct model for simulating process of accommodating network online loads and then adopt genetic algorithm to optimize the model. Due to the heuristic property of evolutional method, the new method is efficient and effective, which is verified by the experiments.展开更多
<div style="text-align:justify;"> This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a s...<div style="text-align:justify;"> This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared. </div>展开更多
It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random...It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.展开更多
As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of sing...As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.展开更多
基金Supported by the National Natural Science Foundation of China(61672433)the Fundamental Research Fund for Shenzhen Science and Technology Innovation Committee(201703063000511,201703063000517)+1 种基金the National Cryptography Development Fund(MMJJ20170210)the Science and Technology Project of State Grid Corporation of China(522722180007)
文摘This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training set are optimally divided into several non-overlapping groups to maximize the label distinguishability in every group. Multiple classifiers are trained separately and ensembled for label predictions. Experimental results show significant improvement over previous weak label learning algorithms.
文摘In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.
基金supported by the National Natural Science Foundation of China(5110505261173163)the Liaoning Provincial Natural Science Foundation of China(201102037)
文摘In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.
基金supported in part by the National Natural Science Foundation of China(61379049,61772120)
文摘Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.
基金Supported by the Opening Fund of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education (93K-17-2010-K02)the Opening Fund of Key Discipline of Computer Soft-Ware and Theory of Zhejiang Province at Zhejiang Normal University (ZSDZZZZXK05)
文摘Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.
基金Supported by Australian Research Council Discovery(DP130102691)the National Science Foundation of China(61302157)+1 种基金China National 863 Project(2012AA12A308)China Pre-research Project of Nuclear Industry(FZ1402-08)
文摘It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.
文摘The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.
基金supported by the National Natural Science Foundation of China(Grant No.61178086)Science and Technology Program of Guangzhou,China(Grant No.2012J4300138)Foundation for Distinguished Young Talents in South China Normal University,China.(Grant No.2012KJ010).
文摘We demonstrate the feasibility of simultancous multi-probe detection for an optcal-resolution photoacoustic microscopy(OR-PAM)system.OR-P AM has elicited the attention of biomedical imaging researchers because of its optical absorption contrast and high spatial resolution with great imaging depth.OR-PAM allows label-free and noninvasive imaging by maximizing the optical absorption of endogenous biomolecules.However,given the inadequate absoption of some biomolcules,detection sensitivity at the same incident intensity requires improvement.In this study,a modulated continuous wave with power density less than 3mW/cm^(2)(1/4 of the ANSI safety limit)excited the weak photoacoustic(PA)signals of biological cells.A microcavity traneducer is developed based on the bulk modulus of gas five orders of magnitude lower than that of solid;air pressure variation is inversely proportional to cavity volume at the same temperature increase.Considering that a PA wave expands in various directions,detecting PA signals from different positions and adding them together can increase detection sensitivity and signal-to-noise ratio.Therefore,we employ four detectors to acquire tiny PA signals simul-taneously.Experimental results show that the developed OR-PAM system allows the label-free imaging of cells with weak optical absorption.
基金Supported by Australian Research Council Discovery(DP130102691)the National Science Foundation of China(61302157)+1 种基金China National 863 Project(2012AA12A308)China Pre-research Project of Nuclear Industry(FZ1402-08)
文摘In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels.The paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation criteria.The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods.
基金supported by the National Natural Science Foundation of China(61472305)the Science Research Program,Xi’an,China(2017073CG/RC036CXDKD003)the Aeronautical Science Foundation of China(20151981009)
文摘Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.
基金Project supported by the National Natural Science Foundation of China(Grant No.60605012)the Natural Science Foundation of Shanghai(Grant No.08ZR1408200)+1 种基金the Open Project Program of the National Laboratory of Pattern Recognition of China(Grant No.08-2-16)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.
基金Supported by the National Natural Science Foundation of China(No.61602191,61672521,61375037,61473291,61572501,61572536,61502491,61372107,61401167)the Natural Science Foundation of Fujian Province(No.2016J01308)+3 种基金the Scientific and Technology Funds of Quanzhou(No.2015Z114)the Scientific and Technology Funds of Xiamen(No.3502Z20173045)the Promotion Program for Young and Middle aged Teacher in Science and Technology Research of Huaqiao University(No.ZQN-PY418,ZQN-YX403)the Scientific Research Funds of Huaqiao University(No.16BS108)
文摘Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.
基金This work was supported by the National Natural Science Foundation of China (No10371097)Open Project of Com-putational Key Laboratory in Yunnan Provice
文摘A new method for multi-protocol label switching is presented in this study, whose core idea is to construct model for simulating process of accommodating network online loads and then adopt genetic algorithm to optimize the model. Due to the heuristic property of evolutional method, the new method is efficient and effective, which is verified by the experiments.
文摘<div style="text-align:justify;"> This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared. </div>
基金supported by the National High Technology Research and Development Program of China (No.2014AA7014061)the National Natural Science Foundation of China (No.61501484)
文摘It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.
文摘As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.
