Abstract—Machine learn methods have been widely used for classification and diagnosis of diseases for increasing its accuracy and efficiency. The kernel extreme learning machine is being increasingly used algorithm to training single layer forward neural network as that this network is given the weights between input and hidden layers, and the bias parameter of each hidden node. In order to obtain more stable and accurate model, an artificial bee colony algorithm is used to pre-train parameters of kernel parameter and penalty parameter. weight and bias. In this paper, an artificial bee colony based kernel extreme learning machine is proposed to classify medical datasets. This proposed method is called ABC-KELM. In experiments, we use two benchmark datasets that are Breast cancer and Parkinson disease from the UCI repository to evaluate the effectiveness and classification accuracy. The experimental results reveal that the ABC-KELM can obtain satisfactory classification results.

Index Terms—Machine learning, kernel extreme learning machine, artificial bee colony algorithm, UCI repository

Ming-Huwi Horng and Jian-Ying Cheng are with Department of Computer Science and Information Engineering, National PingTung University, Taiwan. Yu-Lun Hung is with Department of Business Computing, National Kaohsiung University of Science and Technology, Taiwan. Yu-Cheng Hung is with Computer and Intelligent Robot Program for Bachelor Degree, National PingTung University, Taiwan. Yung-Nien Sun is with Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan. Pongpon Nilaphruek is with Computer Science Department, Rajamangala University of Technology Thanyaburi, Thailand.

Cite: Ming-Huwi Horng, Jian-Ying Cheng, Yu-Lun Hung, Yu-Cheng Hung, Yung-Nien Sun, Pongpon Nilaphruek, "Parameter Optimization of Kernel Extreme Learning Machine Using Artificial Bee Colony Algorithm and Its Application for Disease Classification," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 10, no. 3, pp. 127-136, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).