Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete ...Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete problem and the genetic algorithm is an appropriate method to solve it. In order to achieve optimal discretization, first the choice of the initial set of cut set is discussed, because a good initial cut set can enhance the efficiency and quality of the follow-up algorithm. Second, an effective heuristic genetic algorithm for discretization of continuous attributes of the decision table is proposed, which takes the significance of cut dots as heuristic information and introduces a novel operator to maintain the indiscernibility of the original decision system and enhance the local research ability of the algorithm. So the algorithm converges quickly and has global optimizing ability. Finally, the effectiveness of the algorithm is validated through experiment.展开更多
Knowledge reduction is an important issue when dealing with huge amounts of data. And it has been proved that computing the minimal reduct of decision system is NP-complete. By introducing heuristic information into g...Knowledge reduction is an important issue when dealing with huge amounts of data. And it has been proved that computing the minimal reduct of decision system is NP-complete. By introducing heuristic information into genetic algorithm, we proposed a heuristic genetic algorithm. In the genetic algorithm, we constructed a new operator to maintaining the classification ability. The experiment shows that our algorithm is efficient and effective for minimal reduct, even for the special example that the simple heuristic algorithm can’t get the right result.展开更多
Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough se...Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.展开更多
A new method based on rough set theory and genetic algorithm was proposedto predict the rock burst proneness. Nine influencing factors were first selected, and then,the decision table was set up. Attributes were reduc...A new method based on rough set theory and genetic algorithm was proposedto predict the rock burst proneness. Nine influencing factors were first selected, and then,the decision table was set up. Attributes were reduced by genetic algorithm. Rough setwas used to extract the simplified decision rules of rock burst proneness. Taking the practical engineering for example, the rock burst proneness was evaluated and predicted bydecision rules. Comparing the prediction results with the actual results, it shows that theproposed method is feasible and effective.展开更多
This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the inpu...This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infrastructure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit (FCCU), and satisfying results are obtained.展开更多
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.展开更多
Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem ...Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.展开更多
In rough communication,because each agent has a different language and can not provide precise communication to each other,the concept translated among multi-agents will loss some information,and this results in a les...In rough communication,because each agent has a different language and can not provide precise communication to each other,the concept translated among multi-agents will loss some information,and this results in a less or rougher concept.With different translation sequences the amount of the missed knowledge is varied.Theλ-optimal translation sequence of rough communication,which concerns both every agent and the last agent taking part in rough communication to get information as much as he(or she)can,is given.In order to get theλ-optimal translation sequence,a genetic algorithm is used.Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.展开更多
Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learn...Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).展开更多
In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are p...In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.展开更多
In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate app...In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.展开更多
Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new meth...Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new method is proposed to improve those typical rough set based heuristic algorithms for data discretization,by utilizing decision information to reduce the scales of candidate cuts,and by more reasonably measuring cut significance with a new conception of cut selection probability.Simulations demonstrate that compared with other typical discretization algorithms based on the rough set theory,the proposed method is more capable and valid to discretize continuous information systems.It can effectively improve the predictive accuracies of information systems while still conceptually keeping their consistency.展开更多
The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of ...The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.展开更多
文摘Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete problem and the genetic algorithm is an appropriate method to solve it. In order to achieve optimal discretization, first the choice of the initial set of cut set is discussed, because a good initial cut set can enhance the efficiency and quality of the follow-up algorithm. Second, an effective heuristic genetic algorithm for discretization of continuous attributes of the decision table is proposed, which takes the significance of cut dots as heuristic information and introduces a novel operator to maintain the indiscernibility of the original decision system and enhance the local research ability of the algorithm. So the algorithm converges quickly and has global optimizing ability. Finally, the effectiveness of the algorithm is validated through experiment.
文摘Knowledge reduction is an important issue when dealing with huge amounts of data. And it has been proved that computing the minimal reduct of decision system is NP-complete. By introducing heuristic information into genetic algorithm, we proposed a heuristic genetic algorithm. In the genetic algorithm, we constructed a new operator to maintaining the classification ability. The experiment shows that our algorithm is efficient and effective for minimal reduct, even for the special example that the simple heuristic algorithm can’t get the right result.
基金Project supported by the National Basic Research Program (973)of China (No. 2002CB312106), China Postdoctoral Science Founda-tion (No. 2004035715), the Science & Technology Program of Zhe-jiang Province (No. 2004C31098), and the Postdoctoral Foundation of Zhejiang Province (No. 2004-bsh-023), China
文摘Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.
基金Supported by the Youth Science Foundation of North China University of Water Conservancy and Electric Power(HSQJ2009016)
文摘A new method based on rough set theory and genetic algorithm was proposedto predict the rock burst proneness. Nine influencing factors were first selected, and then,the decision table was set up. Attributes were reduced by genetic algorithm. Rough setwas used to extract the simplified decision rules of rock burst proneness. Taking the practical engineering for example, the rock burst proneness was evaluated and predicted bydecision rules. Comparing the prediction results with the actual results, it shows that theproposed method is feasible and effective.
基金Sponsored by the National High Technology Research and Development Program of China (Grant No.G2001 AA413130).
文摘This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infrastructure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit (FCCU), and satisfying results are obtained.
文摘Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
文摘Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.
基金supported by the National Natural Science Foundation of China(61070241)the Natural Science Foundation of Shandong Province(ZR2010FM035)the Science and Technology Foundation of University of Jinan(XKY1031,XKY0808)
文摘In rough communication,because each agent has a different language and can not provide precise communication to each other,the concept translated among multi-agents will loss some information,and this results in a less or rougher concept.With different translation sequences the amount of the missed knowledge is varied.Theλ-optimal translation sequence of rough communication,which concerns both every agent and the last agent taking part in rough communication to get information as much as he(or she)can,is given.In order to get theλ-optimal translation sequence,a genetic algorithm is used.Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.
文摘Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).
基金This project is partially supported by Science Research Funding from the Education Department of Liaoning Province, China (No.J9906065).
文摘In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.
文摘In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.
基金supported by the Program for New Century Excellent Talents in University, National Natural Science Foundation of China (60573068, 60773113)Natural Science Foundation of Chongqing of China (2005BA2003, 2008BA2017)+1 种基金Starting Research Foundation of Ministry of Education for Chinese Overseas Returnees ([2007]1108)Scientific Research Foundation of Chongqing University of Posts and Telecommunications (A2006-05)
文摘Data discretization contributes much to the induction of classification rules or trees by machine learning methods.The rough set theory is a valid tool for discretizing continuous information systems.Herein,a new method is proposed to improve those typical rough set based heuristic algorithms for data discretization,by utilizing decision information to reduce the scales of candidate cuts,and by more reasonably measuring cut significance with a new conception of cut selection probability.Simulations demonstrate that compared with other typical discretization algorithms based on the rough set theory,the proposed method is more capable and valid to discretize continuous information systems.It can effectively improve the predictive accuracies of information systems while still conceptually keeping their consistency.
文摘The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.
