A Parallel Genetic Algorithm for Solving the Inverse Problem of Support Vector Machines

An ensemble of classifiers is a system consisting of multiple member classifiers which are trained individually and whose outcomes are aggregated into an overall outcome for a testing data instance. Voting is a common approach used to aggregate outcomes generated by member classifiers. Ensembles based on weighted voting have been studied for some time. However, the focus of most studies is more on weight assignment rather than on weight adjustment, whose basic idea is to increase the weights of votes from member classifiers performing better on data instances of higher difficulty. In this paper, we present our study on adjustment functions in each of which both the performance of a member classifier and the difficulty of a data set are determined nonlinearly. We report results from experiments conducted on several data sets, demonstrating the potential of the studied functions.

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A Parallel Genetic Algorithm for Solving the Inverse Problem of Support Vector Machines

Cited by 3 publications

References 7 publications

Mass classification of benign and malignant with a new twin support vector machine joint $${l_{2,1}}$$ l 2 , 1 -norm

Mass classification of benign and malignant with a new twin support vector machine joint $${l_{2,1}}$$ l 2 , 1 -norm

A Hyper-solution SVM Classification Framework: Application to On-line Aircraft Structural Health Monitoring

On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers

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