NDLI: A new approach for binary feature selection and combining classifiers

Content Provider	IEEE Xplore Digital Library
Author	Asaithambi, A. Valev, V. Krzyzak, A. Zeljkovic, V.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput., Univ. of North Florida, Jacksonville, FL, USA (Asaithambi, A.; Valev, V.) \|\| Sch. of Eng. & Comput. Sci., New York Inst. of Technol., Nanjing, China (Zeljkovic, V.) \|\| Dept. of Comput. Sci. & Software Eng., Concordia Univ., Montreal, QC, Canada (Krzyzak, A.)
Abstract	This paper explores feature selection and combining classifiers when binary features are used. The concept of Non-Reducible Descriptors (NRDs) for binary features is introduced. NRDs are descriptors of patterns that do not contain any redundant information. The underlying mathematical model for the present approach is based on learning Boolean formulas which are used to represent NRDs as conjunctions. Starting with a description of a computational procedure for the construction of all NRDs for a pattern, a two-step solution method is presented for the feature selection problem. The method computes weights of features during the construction of NRDs in the first step. The second step in the method then updates these weights based on repeated occurrences of features in the constructed NRDs. The paper then proceeds to present a new procedure for combining classifiers based on the votes computed for different classifiers. This procedure uses three different approaches for obtaining the single combined classifier, using majority, averaging, and randomized vote.
Starting Page	681
Ending Page	687
File Size	682059
Page Count	7
File Format	PDF
ISBN	9781479953127
e-ISBN	9781479953134
DOI	10.1109/HPCSim.2014.6903754
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Hamming distance Diversity reception Educational institutions Pattern recognition Mathematical model Mutual information
Content Type	Text
Resource Type	Article

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