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An Ensemble SVM using Entropy-Based Attribute Selection

An Ensemble SVM using Entropy-Based Attribute Selection

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2010 Chinese Control and Decision Conference

作者： ruhai lei,xiaoxiao kong,xuesong wang school of information and electrical engineering,china university of mining and technology,xuzhou,jiangsu 221116 china

＜正＞In order to improve the generalization performance of support vector machine（SVM）,a kind of ensemble SVM using an entropy-based attribute selection method was *** entropy metric based on similarity between objects was designed to evaluate the importance degree of each attribute and so as to obtain a set of important attributes. Based on the set of important attributes,the Bagging method was used to generate sub-SVMs and then the majority voting rule was adopted to obtain the final ensemble result of all *** proposed ensemble method can avoid destructing the attribute relativity or attribute dependence by selecting an attribute subset from original attribute space *** performance of single SVM can be improved and the diversity between sub-SVMs can also be *** results on UCI testing datasets show that the proposed ensemble method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.

关键词： support vector machine ensemble learning attribute selection entropy

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Support Vector Machine Ensemble Based on Independent Component Analysis and Fuzzy Kernel Clustering

Support Vector Machine Ensemble Based on Independent Compone...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Yong Ma is with the school of information and electrical engineering,china university of mining and technology,xuzhou,jiangsu,221116,china.xiaoxiao kong is with the school of information and electrical engineering,china university of mining and technology,xuzhou,jiangsu,221116,china.xuesong wang is with the school of information and electrical engineering,china university of mining and technology,xuzhou,jiangsu,221116,china

In order to improve the generalization performance of support vector machine （SVM）,a support vector machine ensembling method based on independent component analysis （ICA） and fuzzy kernel clustering （FKC） was *** ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features,the performance of single SVM can be improved when the SVM was trained on these independent features;The FKC method uses kernel function to expand the feature space,so that the clustering is more accurate and the diversity between sub-SVMs can also be *** results on UCI datasets show that the proposed ensembling method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.

关键词： Support vector machine Ensemble learning Independent component analysis Fuzzy kernel clustering

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