Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem

Datasets obtained from the real world are far from balanced, particularly for disease datasets, since such datasets are usually highly skewed having a few minority classes apart from one or more prominent majority classes. In this research, we put forward the novel hybrid architecture to handle imba...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 11; no. 7; pp. 1423 - 1451
Main Authors: Nalluri, MadhuSudana Rao, Kannan, Krithivasan, Gao, Xiao-Zhi, Roy, Diptendu Sinha
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2020
Springer Nature B.V
Subjects:
Artificial Intelligence
Cancer
Classification
Complex Systems
Computational Intelligence
Control
Datasets
Engineering
Evolutionary algorithms
Genetic algorithms
Machine learning
Mechatronics
Multiple objective analysis
Neural networks
Optimization
Original Article
Parameter sensitivity
Pattern Recognition
Robotics
Sensitivity
Statistical tests
Support vector machines
Systems Biology
Totally uni-modular matrix
Multi-objective optimization
SVM
Limit-points
Evolutionary algorithm
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:Datasets obtained from the real world are far from balanced, particularly for disease datasets, since such datasets are usually highly skewed having a few minority classes apart from one or more prominent majority classes. In this research, we put forward the novel hybrid architecture to handle imbalanced binary disease datasets that arrives upon the efficient combination of Support vector machine (SVM) classifier’s sensitive parameter values for improved performance of SVM by means of an Evolutionary algorithm (EA), namely monarch butterfly optimization (MBO). In this paper, MBO is used to enumerate three objectives, namely prediction accuracy (PAC), sensitivity (SEN), specificity (SPE). Additionally, we propose a Totally uni-modular matrix (TUM) and limit points based non-dominated solutions selection for deciding local and global search and to generate an efficient initial population respectively. Since these two greatly affect the performance of EAs, the performance of the proposed hybrid architecture is tested on 18 disease datasets having binary class labels and the results obtained demonstrate improvements using the proposed method. For the majority of the datasets, either 100% sensitivity and/or specificity were attained. Moreover, pertinent statistical tests were carried out to ascertain the performances obtained.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-019-01047-9