Selective Ensemble Based on Transformation of Classifiers Used SPCA
Abstract
The diversity and the accuracy are two important ingredients for ensemble generalization error in an ensemble classifiers system. Nevertheless enhancing the diversity is at the expense of decreasing the accuracy of classifiers, thus balancing the diversity and the accuracy is crucial for constructing a good ensemble method. In the paper, a new ensemble method is proposed that selecting classifiers to ensemble via the transformation of individual classifiers based on diversity and accuracy. In the proposed method, the transformation of classifiers is made to produce new individual classifiers based on original classifiers and the true labels, in order to enhance diversity of an ensemble. The transformation approach is similar to principal component analysis (PCA), but it is essentially different between them that the proposed method employs the true labels to construct the covariance matrix rather than the mean of samples in PCA. Then a selecting rule is constructed based on two rules of measuring the classification performance. By the selecting rule, some available new classifiers are selected to ensemble in order to ensure the accuracy of the ensemble with selected classifiers. In other words, some individuals with poor or same performance are eliminated. Particularly, a new classifier produced by the transformation is equivalent to a linear combination of original classifiers, which indicates that the proposed method enhances the diversity by different transformations instead of constructing different training subsets. The experimental results illustrate that the proposed method obtains the better performance than other methods, and the kappa-error diagrams also illustrate that the proposed method enhances the diversity compared against other methods.
