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Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution

Publication Type : Journal Article

Publisher : Computers & Electrical Engineering .

Source : Computers & Electrical Engineering, Volume 67, p.483 - 496 (2018)

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Keywords : Competitive collection, Data mining, Disease classifier, EHO, Initial population generation, Medical engineering data, MOEA/D, NSGA-II, Sequential Minimal Optimization (SMO), Support vector machine (SVM).

Campus : Coimbatore

School : School of Engineering

Department : Mathematics

Year : 2018

Abstract : In this research, intelligent classifiers for disease diagnosis are designed that use classifier parameters, such as cost, tolerance, gamma and epsilon, with multi-objective evolutionary algorithms. The multiple objective functions are prediction accuracy, sensitivity and specificity. This paper employs a Sequential Minimal Optimization (SMO), a variant of the classical Support Vector Machine (SVM), as the base classifier in conjunction with three popular evolutionary algorithms (EA), namely, Elephant Herding Optimization (EHO), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), for parameter evolution. A new cuboids based initial population generation mechanism was also introduced to hybridize EHO, called CEHO. The performance of CEHO is compared with the other three EAs (EHO, MOEA/D and NSGA-II) over 17 medical engineering datasets, and pertinent statistical tests were conducted to substantiate their performances. The results demonstrate that the proposed CEHO exhibit better to competitive results across all datasets.

Cite this Research Publication : Madhusudana Rao Nalluri, Krithivasan Kannan, Xiao-Zhi Gao, and Diptendu Sinha Roy, “Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution”, Computers & Electrical Engineering, vol. 67, pp. 483 - 496, 2018.

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