Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines

Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetting phenomenon. In our previous work, integrating the SVM classifiers into an ensemble framework using Learn++ (SVMLearn++) [1], we have shown that the SVM classifiers can in fact be equipped with the incremental learning capability. However, Learn++ suffers from an inherent out-voting problem: when asked to learn new classes, an unnecessarily large number of classifiers are generated to learn the new classes. In this paper, we propose a new ensemble based incremental learning approach using SVMs that is based on the incremental Learn++.MT algorithm. Experiments on the real-world and benchmark datasets show that the proposed approach can reduce the number of SVM classifiers generated, thus reduces the effect of outvoting problem. It also provides performance improvements over previous approach.

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Zeki Erdem, Robi Polikar, Fikret S. Gürgen, Nejat Yumusak
• Keywords: ICANN 2005, Incremental Learning, Large Number, Svm Classifiers