Reducing the effect of out-voting problem in ensemble based incremental support vector machines
15th International Conference on Artificial Neural Networks: Formal Models and Their Applications, ICANN 2005, Warszawa, Polonya, 11 - 15 Eylül 2005, cilt.3697 LNCS, ss.607-612, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 3697 LNCS
- Doi Numarası: 10.1007/11550907_96
- Basıldığı Şehir: Warszawa
- Basıldığı Ülke: Polonya
- Sayfa Sayıları: ss.607-612
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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 out-voting problem. It also provides performance improvements over previous approach. © Springer-Verlag Berlin Heidelberg 2005.