期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 141, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.105137
关键词
Sine cosine algorithm; Disperse foraging strategy; Kernel extreme learning machine; Parameter optimization; Medical diagnosis
类别
资金
- National Natural Science Foundation of China [62076185, U1809209]
- Zhejiang Provincial Natural Science Foundation of China [LY21F020030]
- Wenzhou Science AMP
- Technology Bureau [2018ZG016]
A new parameter optimization strategy based on DFSCA is proposed and integrated into KELM to establish DFSCA-KELM, improving optimization performance. Experimental results demonstrate the effectiveness of the model in medical diagnosis.
Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.
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