Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

Ch Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung-Bae Cho

Research output: Contribution to journalArticle

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.

Original languageEnglish
JournalApplied Computing and Informatics
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Teaching
Classifiers
Neural networks
Labels

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Science Applications

Cite this

@article{cfe27f747b5b4ab2a1fdfa7d826e1dd4,
title = "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.",
author = "Dash, {Ch Sanjeev Kumar} and Behera, {Ajit Kumar} and Satchidananda Dehuri and Sung-Bae Cho",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.aci.2019.03.001",
language = "English",
journal = "Applied Computing and Informatics",
issn = "2210-8327",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

AU - Dash, Ch Sanjeev Kumar

AU - Behera, Ajit Kumar

AU - Dehuri, Satchidananda

AU - Cho, Sung-Bae

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85063060610&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063060610&partnerID=8YFLogxK

U2 - 10.1016/j.aci.2019.03.001

DO - 10.1016/j.aci.2019.03.001

M3 - Article

AN - SCOPUS:85063060610

JO - Applied Computing and Informatics

JF - Applied Computing and Informatics

SN - 2210-8327

ER -