Title: An automated ontology learning for benchmarking classifier models through gain-based relative-non-redundant feature selection: a case-study with erythemato-squamous disease

Authors: Sivasankari Sivasubramanian; Shomona Gracia Jacob

Addresses: Department of CSE, Hindustan Institute of Technology and Science, Chennai, India ' Department of CSE, SSN College of Engineering, Anna University, Chennai, India

Abstract: Erythemato-squamous disease (ESD) is one of the complex diseases in the dermatology field, the diagnosis of which is challenging, due to common morphological features and often leads to inconsistent results. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for prediction of erythemato-squamous disease through data mining techniques was believed to yield a clear representation of the relationships between the disease, symptoms and course of treatment. However, the classification accuracy required to be high in order to obtain a precise ontology. This required identifying the correct set of optimal features required to predict ESD. This paper proposes the Gain based Relative-Non-Redundant Attribute selection approach for diagnosis of ESD. This methodology yielded 98.1% classification accuracy with Adaboost algorithm that executed J48 as the base classifier. The feature selection approach revealed an optimal feature set comprising of 19 selected features.

Keywords: ontology; feature selection; classifier; web ontology language; gain base; erythemato-squamous.

DOI: 10.1504/IJBIDM.2020.106132

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.3, pp.261 - 278

Received: 17 Apr 2017
Accepted: 02 Sep 2017

Published online: 13 Feb 2020 *

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