Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis. (August 2021) - Wu, Shubiao; Mao, Peng; Li, Rizeng; Cai, Zhennao; Heidari, Ali Asghar; Xia, Jianfu; Chen, Huiling; Mafarja, Majdi; Turabieh, Hamza; Chen, Xiaowei
Abstract: Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com . Highlights: An advanced sine cosine algorithm (LSCA) is proposed to improve the performance of the original algorithm. A machine learning method called LSCA-FKNN is proposed with adaptive selection of features and optimization parameters. The performance of LSCA is verified on benchmark functions. The proposed LSCA-FKNN is used for the diagnosis of lupus nephritis, which provides a feasible solution for clinical decision making.