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.
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Publishers note: The publisher wishes to inform readers that the article “Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features” was originally published by the previous publisher of Applied Computing and Informatics and the pagination of this article has been subsequently changed. There has been no change to the content of the article. This change was necessary for the journal to transition from the previous publisher to the new one. The publisher sincerely apologises for any inconvenience caused. To access and cite this article, please use Kumar Dash, Ch. S., Kumar Behera, A., Dehuri, S., Cho, S. B. (2019), “Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features”, Applied Computing and Informatics. Vol. 18 No. 1/2, pp. 153-164. The original publication date for this paper was 18/03/2019.
© 2019, Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri and Sung-Bae Cho.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications