MIFS-ND: A mutual information-based feature selection method
MIFS-ND: A mutual information-based feature selection method
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DOI：
10.1016/j.eswa.2014.04.019
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发表时间：
2014-10-15
影响因子：
8.5
通讯作者：
Kalita, J. K.
中科院分区：
文献类型：
--
作者：
Hoque, N.;Bhattacharyya, D. K.;Kalita, J. K.
特征选择用于选择相关特征的子集，以便对数据进行有效分类。在高维数据分类中，分类器的性能往往取决于用于分类的特征子集。本文介绍了一种基于互信息的贪婪特征选择方法。该方法结合特征-特征互信息和特征-类互信息来寻找最优的特征子集，以最小化冗余并最大化特征之间的相关性。使用多个分类器对多个数据集评估所选特征子集的有效性。我们的方法在分类精度和执行时间性能方面的性能，已被发现显着高的12个现实生活中的数据集的不同维度和实例数相比，与几个竞争的特征选择技术。(C)2014爱思唯尔有限公司版权所有。
Feature selection is used to choose a subset of relevant features for effective classification of data. In high dimensional data classification, the performance of a classifier often depends on the feature subset used for classification. In this paper, we introduce a greedy feature selection method using mutual information. This method combines both feature-feature mutual information and feature-class mutual information to find an optimal subset of features to minimize redundancy and to maximize relevance among features. The effectiveness of the selected feature subset is evaluated using multiple classifiers on multiple datasets. The performance of our method both in terms of classification accuracy and execution time performance, has been found significantly high for twelve real-life datasets of varied dimensionality and number of instances when compared with several competing feature selection techniques. (C) 2014 Elsevier Ltd. All rights reserved.