A Feature Selection Method Combining Filter and Wrapper Approaches for Medical Dataset Classification

The curse of dimensionality is a problem that arises in datasets containing high-dimensional features. One of the primary methods used to address this issue is feature selection (FS), an effective technique for reducing insignificant features in a dataset. In this study, a two-stage approach called JASAL, which combines Filter and Wrapper FS methods, is proposed for high-dimensional medical datasets. In the first stage, preprocessing is applied to the high-dimensional datasets, and the ReliefF filter method is selected for this purpose. In the subsequent stage, continuous optimization algorithms, such as the Sine-Cosine Algorithm (SCA) and JAYA algorithms, are hybridized for binary optimization. A wrapper method that stochastically selects one of these two algorithms is used. Additionally, to overcome the local optimum problem of the JAYA algorithm, the L & eacute;vy flight strategy is incorporated into JAYA's existing solution update strategy. To evaluate the performance of the JASAL algorithm, experiments are conducted on eight medical datasets, seven of which are high-dimensional. The results of the proposed algorithm are compared with 11 other metaheuristic algorithms. Various metrics, such as mean accuracy, mean number of selected features, and standard deviation, are considered in the experiments. The experimental results reveal that the JASAL algorithm achieves higher average accuracy than the compared algorithms. Furthermore, it is observed that these accuracy values are obtained by selecting relatively fewer features.