Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features
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.
Authors: Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung-Bae Cho
Published in: Applied Computing and Informatics (2019)