A Mixed Feature Selection Method Considering Interaction

Zilin Zeng,Rui Zhang,Hongjun Zhang,Youliang Zhang

doi:10.1155/2015/989067

Zilin Zeng, Rui Zhang + Show 2 more

Open Access

https://doi.org/10.1155/2015/989067

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Abstract

Feature interaction has gained considerable attention recently. However, many feature selection methods considering interaction are only designed for categorical features. This paper proposes a mixed feature selection algorithm based on neighborhood rough sets that can be used to search for interacting features. In this paper, feature relevance, feature redundancy, and feature interaction are defined in the framework of neighborhood rough sets, the neighborhood interaction weight factor reflecting whether a feature is redundant or interactive is proposed, and a neighborhood interaction weight based feature selection algorithm (NIWFS) is brought forward. To evaluate the performance of the proposed algorithm, we compare NIWFS with other three feature selection algorithms, including INTERACT, NRS, and NMI, in terms of the classification accuracies and the number of selected features with C4.5 and IB1. The results from ten real world datasets indicate that NIWFS not only deals with mixed datasets directly, but also reduces the dimensionality of feature space with the highest average accuracies.

Highlights

Feature selection plays an important role in pattern recognition and machine learning
The remainder of this paper is structured as follows: Section 2 reviews some basic concepts related to neighborhood rough sets and neighborhood entropy-based information measures; Section 3 provides our definitions of relevant feature, redundant feature, and interactive feature based on neighborhood interaction gain; Section 4 puts forward a neighborhood interaction weight based feature selection algorithm; Section 5 presents the experimental results and analysis to evaluate the effectiveness of the proposed method; and Section 6 lays out our conclusions
We empirically evaluate the performance of our proposed algorithm and present the experimental results in comparison with the other three different types of feature subset selection algorithms applied to ten real world datasets, respectively

Summary

Introduction

Feature selection plays an important role in pattern recognition and machine learning. Rough set theory can be used to find a subset of informative features which preserves the discernible ability from the original features It has been playing an important role in feature selection [24,25,26,27,28,29]. The remainder of this paper is structured as follows: Section 2 reviews some basic concepts related to neighborhood rough sets and neighborhood entropy-based information measures; Section 3 provides our definitions of relevant feature, redundant feature, and interactive feature based on neighborhood interaction gain; Section 4 puts forward a neighborhood interaction weight based feature selection algorithm; Section 5 presents the experimental results and analysis to evaluate the effectiveness of the proposed method; and Section 6 lays out our conclusions

Preliminaries

Proposed Feature Selection Algorithm

Experiments

Conclusions and Future Work