Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features. Issue 1 (4th August 2020)

• Record Type: Journal Article
• Authors: Dash, Ch. Sanjeev Kumar; Kumar Behera, Ajit; Dehuri, Satchidananda; Cho, Sung-Bae
• Is Part Of: Applied computing and informatics. Volume 18:Issue 1/2(2022)
• Page Start: 151
• Page End: 162
• Publication Date: 2020-08-04
• Subjects: Pattern recognition -- Imputation -- Classification -- Radial basis function neural networks -- Teaching learning based optimization -- k-Nearest neighbor; Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2019.03.001
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.