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DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Sarat Chandra Nayak
Copyright: © 2018 |Pages: 26
ISBN13: 9781522528579|ISBN10: 1522528571|EISBN13: 9781522528586
DOI: 10.4018/978-1-5225-2857-9.ch011
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MLA

Dash, Ch. Sanjeev Kumar, et al. "DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features." Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms, edited by Sujata Dash, et al., IGI Global, 2018, pp. 218-243. https://doi.org/10.4018/978-1-5225-2857-9.ch011

APA

Dash, C. S., Behera, A. K., & Nayak, S. C. (2018). DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features. In S. Dash, B. Tripathy, & A. Rahman (Eds.), Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms (pp. 218-243). IGI Global. https://doi.org/10.4018/978-1-5225-2857-9.ch011

Chicago

Dash, Ch. Sanjeev Kumar, Ajit Kumar Behera, and Sarat Chandra Nayak. "DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features." In Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms, edited by Sujata Dash, B.K. Tripathy, and Atta ur Rahman, 218-243. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2857-9.ch011

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Abstract

This chapter presents a novel approach for classification of dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact its size. In this chapter, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. Different feature selection methods, handling missing values and removal of inconsistency to improve the classification accuracy of the proposed model are emphasized. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

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