Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
Department of Pain Medicine, China-Japan Friendship Hospital, Beijing, 100029, China.
Comput Biol Med. 2021 Aug;135:104582. doi: 10.1016/j.compbiomed.2021.104582. Epub 2021 Jun 17.
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.
由于其简单性和有效性，模糊 K-最近邻（FKNN）在文献中被广泛使用。参数对 FKNN 的性能有重要影响。因此，需要调整参数以适应不同的问题。此外，选择更具代表性的特征可以提高 FKNN 的性能。本研究提出了一种基于正弦余弦算法（LSCA）的改进优化技术，该技术引入了一种线性种群规模缩减机制，以提高原始算法的性能。此外，我们基于 LSCA 开发了一个 FKNN 模型，它同时执行特征选择和参数优化。首先，将 LSCA 的搜索性能与经典和改进算法在 IEEE CEC2017 基准测试函数上进行了比较。其次，在三个医学数据集上验证了 LSCA-FKNN 模型的有效性。最后，我们使用提出的 LSCA-FKNN 来预测狼疮肾炎的类别，该模型表现出了有竞争力的结果。任何问题都可以在 https://aliasgharheidari.com 获得在线网络服务支持。
