Reducing the effect of out-voting problem in ensemble based incremental support vector machines

Zeki Erdem, Robi Polikar, Fikret Gurgen, Nejat Yumusak

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations

    Abstract

    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.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages607-612
    Number of pages6
    StatePublished - Dec 1 2005
    Event15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw, Poland
    Duration: Sep 11 2005Sep 15 2005

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3697 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
    Country/TerritoryPoland
    CityWarsaw
    Period9/11/059/15/05

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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