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A Mixed Feature Selection Method Considering Interaction
- Source :
- Mathematical Problems in Engineering, Vol 2015 (2015)
- Publication Year :
- 2015
- Publisher :
- Hindawi Limited, 2015.
-
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.
- Subjects :
- Article Subject
business.industry
lcsh:Mathematics
General Mathematics
Feature vector
Dimensionality reduction
Feature extraction
General Engineering
Kanade–Lucas–Tomasi feature tracker
Feature selection
Pattern recognition
lcsh:QA1-939
computer.software_genre
lcsh:TA1-2040
Feature (computer vision)
Minimum redundancy feature selection
Data mining
Artificial intelligence
Rough set
lcsh:Engineering (General). Civil engineering (General)
business
computer
Mathematics
Subjects
Details
- ISSN :
- 15635147 and 1024123X
- Volume :
- 2015
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....c63ab126e85250123e17d2b1f5718e11