Affiliation:
1. Department of Information Systems Engineering, Ben-Gurion University of the Negev, Israel
2. Department of Industrial Engineering, Tel-Aviv University, Israel
Abstract
Feature selection is the process of identifying relevant features in the dataset and discarding everything else as irrelevant and redundant. Since feature selection reduces the dimensionality of the data, it enables the learning algorithms to operate more effectively and rapidly. In some cases, classification performance can be improved; in other instances, the obtained classifier is more compact and can be easily interpreted. There is much work done on feature selection methods for creating ensemble of classifiers. Thus, these works examine how feature selection can help ensemble of classifiers to gain diversity. This paper examines a different direction, i.e. whether ensemble methodology can be used for improving feature selection performance. In this paper we present a general framework for creating several feature subsets and then combine them into a single subset. Theoretical and empirical results presented in this paper validate the hypothesis that this approach can help to find a better feature subset.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Reference28 articles.
1. Boosting With theL2Loss
2. Ridge Estimators in Logistic Regression
3. B. Chizi, O. Maimon and A. Smilovici, Frontiers in Artificial Intelligence and Applications (IOS press, 2002) pp. 230–236.
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献