Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extraction

Support vector machine (SVM) is a state-of-art classification tool with good accuracy due to its ability to generate nonlinear model. However, the nonlinear models generated are typically regarded as incomprehensible black-box models. This lack of explanatory ability is a serious problem for practic...

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Bibliographic Details
Published in:Knowledge and information systems 2018-04, Vol.55 (1), p.253-274
Main Authors: Kuo, R. J., Huang, S. B. Li, Zulvia, F. E., Liao, T. W.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Computer Science
Data Mining and Knowledge Discovery
Database Management
Decision trees
Feature extraction
Feature selection
Genetic algorithms
Heuristic methods
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Mathematical models
Optimization
Parameter optimization
Parameters
Particle swarm optimization
Regular Paper
Support vector machines
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Summary:Support vector machine (SVM) is a state-of-art classification tool with good accuracy due to its ability to generate nonlinear model. However, the nonlinear models generated are typically regarded as incomprehensible black-box models. This lack of explanatory ability is a serious problem for practical SVM applications which require comprehensibility. Therefore, this study applies a C5 decision tree (DT) to extract rules from SVM result. In addition, a metaheuristic algorithm is employed for the feature selection. Both SVM and C5 DT require expensive computation. Applying these two algorithms simultaneously for high-dimensional data will increase the computational cost. This study applies artificial bee colony optimization (ABC) algorithm to select the important features. The proposed algorithm ABC–SVM–DT is applied to extract comprehensible rules from SVMs. The ABC algorithm is applied to implement feature selection and parameter optimization before SVM–DT. The proposed algorithm is evaluated using eight datasets to demonstrate the effectiveness of the proposed algorithm. The result shows that the classification accuracy and complexity of the final decision tree can be improved simultaneously by the proposed ABC–SVM–DT algorithm, compared with genetic algorithm and particle swarm optimization algorithm.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-017-1083-8