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首页> 外文会议>East-European Conference on Advances in Databases and Information Systems Held Jointly With International Conference on Databases Systems for Advanced Applications, ADBIS-DASFAA 2000, Sep 5-9, 2000, Prague, Czech Republic >Decision Committee Learning with Dynamic Integration of Classifiers
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Decision Committee Learning with Dynamic Integration of Classifiers

机译：具有分类器动态集成的决策委员会学习

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摘要

Decision committee learning has demonstrated spectacular success in reducing classification error from learned classifiers. These techniques develop a classifier in the form of a committee of subsidiary classifiers. The combination of outputs is usually performed by majority vote. Voting, however, has a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then the average classifier will give a wrong prediction, and the majority vote will more probably result in a wrong prediction, Instead of voting, dynamic integration of classifiers can be used, which is based on the assumption that each committee member is best inside certain subareas of the whole feature space. In this paper, the proposed dynamic integration technique is evaluated with AdaBoost and Bagging, the decision committee approaches which have received extensive attention recently. The comparison results show that boosting and bagging have often significantly better accuracy with dynamic integration of classifiers than with simple voting.
机译：决策委员会的学习在减少学习到的分类器的分类错误方面取得了惊人的成功。这些技术以附属分类器委员会的形式开发了一个分类器。产出的组合通常以多数票进行。但是，投票有一个缺点。它无法考虑本地专业知识。当新实例难以分类时，平均分类器将给出错误的预测，而多数表决将更可能导致错误的预测。基于假设，可以使用分类器的动态集成来代替投票每个委员会成员最好在整个要素空间的某些子区域内。在本文中，采用AdaBoost和Bagging对所提出的动态集成技术进行了评估，决策委员会的方法最近受到了广泛的关注。比较结果表明，分类器的动态集成通常比简单投票具有更好的准确性。

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