The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability ...The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. When using the original covering algorithm(CA), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network. By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.展开更多
The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual informatio...The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous.To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.展开更多
Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves elim...Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves eliminating irrelevant,redundant,and noisy features to streamline the classification process.Various methods,from single feature selection techniques to ensemble filter-wrapper methods,have been used in the literature.Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents.Feature selection is inherently multi-objective,balancing the enhancement of feature relevance,accuracy,and the reduction of redundant features.This research presents a two-fold objective for feature selection.The first objective is to identify the top-ranked features using an ensemble of three multi-univariate filter methods:Information Gain(Infogain),Chi-Square(Chi^(2)),and Analysis of Variance(ANOVA).This aims to maximize feature relevance while minimizing redundancy.The second objective involves reducing the number of selected features and increasing accuracy through a hybrid approach combining Artificial Bee Colony(ABC)and Genetic Algorithms(GA).This hybrid method operates in a wrapper framework to identify the most informative subset of text features.Support Vector Machine(SVM)was employed as the performance evaluator for the proposed model,tested on two high-dimensional multiclass datasets.The experimental results demonstrated that the ensemble filter combined with the ABC+GA hybrid approach is a promising solution for text feature selection,offering superior performance compared to other existing feature selection algorithms.展开更多
This paper presents anew way to extract concept that can beused to improve text classification per-formance (precision and recall). Thecomputational measure will be dividedinto two layers. The bottom layercalled docum...This paper presents anew way to extract concept that can beused to improve text classification per-formance (precision and recall). Thecomputational measure will be dividedinto two layers. The bottom layercalled document layer is concernedwith extracting the concepts of parti-cular document and the upper layercalled category layer is with findingthe description and subject concepts ofparticular category. The relevant im-plementation algorithm that dramatic-ally decreases the search space is dis-cussed in detail. The experiment basedon real-world data collected from Info-Bank shows that the approach is supe-rior to the traditional ones.展开更多
The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification ...The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification of these documents with less human interaction has become necessary. In this paper, we introduce a new algorithm called the highest repetition of words in a text document (HRWiTD) to classify the automatic Arabic text. The corpus is divided into a train set and a test set to be applied to proposed classification technique. The train set is analyzed for learning and the learning data is stored in the Learning Dataset file. The category that contains the highest repetition for each word is assigned as a category for the word in Learning Dataset file. This file includes non-duplicate words with the value of higher repetition and categories and they get from all texts in the train set. For each text in the test set, the category of words is assigned to a specific category by using Learning Dataset file. The category that contains the largest number of words is assigned as the predicted category of the text. To evaluate the classification accuracy of the HRWiTD algorithm, the confusion matrix method is used. The HRWiTD algorithm has been applied to convergent samples from six categories of Arabic news at SPA (Saudi Press Agency). As a result, the accuracy of the HRWiTD algorithm is 86.84%. In addition, we used the same corpus with the most popular machine learning algorithms which are C5.0, KNN, SVM, NB and C4.5, and their results of classification accuracy are 52.86%, 52.38%, 51.90%, 51.90% and 30%, respectively. Thus, the HRWiTD algorithm gives better classification accuracy compared to the most popular machine learning algorithms on the selected domain.展开更多
Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages suc...Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.展开更多
The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build t...The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build text modeling and set pulse signal function to get the power spectrum of the text. The specific detail is getting power spectrum from economic field to build spectral library, and then using the method of power spectrum matching algorithm to judge whether the test text belonged to the economic field. The method made text similarity system finish the function of text intelligent classification efficiently and accurately.展开更多
通过检索关键词,指定一个或多个类别标签实现文本的高效组织和自动分类,是发现文档中的隐含关系、推动知识传播和创新的重要途径。然而,检索关键词的获取位置、词性以及选取是否全面等因素,会导致关键词语义信息缺失和关键词识别准确性...通过检索关键词,指定一个或多个类别标签实现文本的高效组织和自动分类,是发现文档中的隐含关系、推动知识传播和创新的重要途径。然而,检索关键词的获取位置、词性以及选取是否全面等因素,会导致关键词语义信息缺失和关键词识别准确性较差;这两大问题,正是影响文档高效、精准自动分类的突出障碍。基于此,论文构建了一个融合TF-IDF(Term Frequency-Inverse Document Frequency)和GloVe(Global Vectors for Word Representation)的文本自动分类系统。该系统首先就词性影响因子和位置权重系数对TF-IDF算法进行改进,以弥补传统TF-IDF算法在关键词识别和语义分析上的不足;其次,使用GloVe模型对关键词集进一步扩充,使文本自动分类的准确率和召回率分别达到92.6%和90.9%;最后,通过实验比对,进一步验证该系统在处理多类别文本自动分类任务中的有效性。展开更多
基金supported by the Fund for Philosophy and Social Science of Anhui Provincethe Fund for Human and Art Social Science of the Education Department of Anhui Province(Grant Nos.AHSKF0708D13 and 2009sk038)
文摘The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. When using the original covering algorithm(CA), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network. By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.
基金Supported by the Scientific and Technological Innovation 2030—Major Project of "New Generation Artificial Intelligence"(2020AAA0109300)。
文摘The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous.To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.
基金supported by Universiti Sains Malaysia(USM)and School of Computer Sciences,USM。
文摘Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves eliminating irrelevant,redundant,and noisy features to streamline the classification process.Various methods,from single feature selection techniques to ensemble filter-wrapper methods,have been used in the literature.Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents.Feature selection is inherently multi-objective,balancing the enhancement of feature relevance,accuracy,and the reduction of redundant features.This research presents a two-fold objective for feature selection.The first objective is to identify the top-ranked features using an ensemble of three multi-univariate filter methods:Information Gain(Infogain),Chi-Square(Chi^(2)),and Analysis of Variance(ANOVA).This aims to maximize feature relevance while minimizing redundancy.The second objective involves reducing the number of selected features and increasing accuracy through a hybrid approach combining Artificial Bee Colony(ABC)and Genetic Algorithms(GA).This hybrid method operates in a wrapper framework to identify the most informative subset of text features.Support Vector Machine(SVM)was employed as the performance evaluator for the proposed model,tested on two high-dimensional multiclass datasets.The experimental results demonstrated that the ensemble filter combined with the ABC+GA hybrid approach is a promising solution for text feature selection,offering superior performance compared to other existing feature selection algorithms.
基金Project supported by the National Natural Science Foundation of China (No. 60082003) and the National High Technology Research and Development Program of China (N0.863-306-ZD03-04-1).
文摘This paper presents anew way to extract concept that can beused to improve text classification per-formance (precision and recall). Thecomputational measure will be dividedinto two layers. The bottom layercalled document layer is concernedwith extracting the concepts of parti-cular document and the upper layercalled category layer is with findingthe description and subject concepts ofparticular category. The relevant im-plementation algorithm that dramatic-ally decreases the search space is dis-cussed in detail. The experiment basedon real-world data collected from Info-Bank shows that the approach is supe-rior to the traditional ones.
文摘The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification of these documents with less human interaction has become necessary. In this paper, we introduce a new algorithm called the highest repetition of words in a text document (HRWiTD) to classify the automatic Arabic text. The corpus is divided into a train set and a test set to be applied to proposed classification technique. The train set is analyzed for learning and the learning data is stored in the Learning Dataset file. The category that contains the highest repetition for each word is assigned as a category for the word in Learning Dataset file. This file includes non-duplicate words with the value of higher repetition and categories and they get from all texts in the train set. For each text in the test set, the category of words is assigned to a specific category by using Learning Dataset file. The category that contains the largest number of words is assigned as the predicted category of the text. To evaluate the classification accuracy of the HRWiTD algorithm, the confusion matrix method is used. The HRWiTD algorithm has been applied to convergent samples from six categories of Arabic news at SPA (Saudi Press Agency). As a result, the accuracy of the HRWiTD algorithm is 86.84%. In addition, we used the same corpus with the most popular machine learning algorithms which are C5.0, KNN, SVM, NB and C4.5, and their results of classification accuracy are 52.86%, 52.38%, 51.90%, 51.90% and 30%, respectively. Thus, the HRWiTD algorithm gives better classification accuracy compared to the most popular machine learning algorithms on the selected domain.
基金supported by the NSFC (Grant Nos. 61772281,61703212, 61602254)Jiangsu Province Natural Science Foundation [grant numberBK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cationInstitutions (PAPD) and Jiangsu Collaborative Innovation Center on AtmosphericEnvironment and Equipment Technology (CICAEET).
文摘Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.
文摘The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build text modeling and set pulse signal function to get the power spectrum of the text. The specific detail is getting power spectrum from economic field to build spectral library, and then using the method of power spectrum matching algorithm to judge whether the test text belonged to the economic field. The method made text similarity system finish the function of text intelligent classification efficiently and accurately.
文摘通过检索关键词,指定一个或多个类别标签实现文本的高效组织和自动分类,是发现文档中的隐含关系、推动知识传播和创新的重要途径。然而,检索关键词的获取位置、词性以及选取是否全面等因素,会导致关键词语义信息缺失和关键词识别准确性较差;这两大问题,正是影响文档高效、精准自动分类的突出障碍。基于此,论文构建了一个融合TF-IDF(Term Frequency-Inverse Document Frequency)和GloVe(Global Vectors for Word Representation)的文本自动分类系统。该系统首先就词性影响因子和位置权重系数对TF-IDF算法进行改进,以弥补传统TF-IDF算法在关键词识别和语义分析上的不足;其次,使用GloVe模型对关键词集进一步扩充,使文本自动分类的准确率和召回率分别达到92.6%和90.9%;最后,通过实验比对,进一步验证该系统在处理多类别文本自动分类任务中的有效性。
