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 language | English |
|---|---|
| Journal | Applied Computing and Informatics |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Information Systems
- Computer Science Applications
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Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features. / Dash, Ch Sanjeev Kumar; Behera, Ajit Kumar; Dehuri, Satchidananda; Cho, Sung-Bae.
In: Applied Computing and Informatics, 01.01.2019.Research output: Contribution to journal › Article
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 -