In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining （HARM） algorithm based on support vector machi...In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining （HARM） algorithm based on support vector machines （SVMs） is proposed. Hyperrectangles rules are constructed on the base of prototypes and support vectors （SVs） under some heuristic limitations. The proposed algorithm is applied to a simulated moving bed （SMB） paraxylene （PX） adsorption process. The relationships between the key process variables and some objective variables such as purity, recovery rate of PX are obtained. Using existing domain knowledge about PX adsorption process, most of the obtained association rules can be explained.展开更多
In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM...In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.展开更多
This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data...This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference system.Theoretical analysis and comparative tests are performed comparing with other fuzzy systems.Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60421002)National Outstanding Youth Science Foundation of China (No. 60025308)the New Century 151 Talent Project of Zhejiang Province.
文摘In this paper, a novel data mining method is introduced to solve the multi-objective optimization problems of process industry. A hyperrectangle association rule mining （HARM） algorithm based on support vector machines （SVMs） is proposed. Hyperrectangles rules are constructed on the base of prototypes and support vectors （SVs） under some heuristic limitations. The proposed algorithm is applied to a simulated moving bed （SMB） paraxylene （PX） adsorption process. The relationships between the key process variables and some objective variables such as purity, recovery rate of PX are obtained. Using existing domain knowledge about PX adsorption process, most of the obtained association rules can be explained.
基金Supported by the National High Technology Research and Development Program of China （No. 2007AA01Z132） the National Natural Science Foundation of China （No.60775035, 60933004, 60970088, 60903141）+1 种基金 the National Basic Research Priorities Programme （No. 2007CB311004） the National Science and Technology Support Plan （No.2006BAC08B06）.
文摘In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.
基金the Shanghai Sciences and Technology Committee under Grant No.08DZ1202500 (No.08DZ1202502)the Young Faculty Research Grant of Shanghai Maritime Universitythe Shanghai Young Faculty Research Grant (No.shs08032)
文摘This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference system.Theoretical analysis and comparative tests are performed comparing with other fuzzy systems.Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.
