A new approach for binary feature selection and combining classifiers
Document Type
Conference Proceeding
Publication Date
9-18-2014
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.
Publication Title
Proceedings of the 2014 International Conference on High Performance Computing and Simulation, HPCS 2014
First Page
681
Last Page
687
Digital Object Identifier (DOI)
10.1109/HPCSim.2014.6903754
ISBN
9781479953127
Citation Information
Asaithambi, Valev, V., Krzyzak, A., & Zeljkovic, V. (2014). A new approach for binary feature selection and combining classifiers. 2014 International Conference on High Performance Computing & Simulation (HPCS), 681–687. https://doi.org/10.1109/HPCSim.2014.6903754