A Modified U‐Shaped Transfer Function: Applied to Classify Parkinson'S Disease

ABSTRACT Transfer functions have a very important role in metaheuristic optimization‐based feature selection algorithms as these functions map the continuous search space into binary space. The U‐shaped transfer function (UTF) is one of the transfer functions used to solve the problem of feature selection. However, the UTF requires the selection of parametric values, which can vary for different types of data. To address this issue, an approach to select the parameters of the UTF has been proposed based on a time‐varying adaption method, resulting in the modified U‐shaped transfer function (MUTF). Furthermore, a methodology has been proposed to enhance feature selection and classification for Parkinson's disease by utilizing z‐score normalization in conjunction with a modified U‐shaped transfer function and the binary self‐adaptive bald eagle search (MUTF‐SABES) optimization algorithm. The z‐score normalization has been used to mitigate issues caused by outliers. Also, the performance of the k nearest neighbor classifier is improved by selecting an optimal parameter value using the proposed MUTF‐SABES algorithm. The effectiveness of the proposed methodology is validated on seven different Parkinson's disease datasets and compared with five state‐of‐the‐art optimization algorithms: Salp Swarm algorithm, Harris Hawks optimization, equilibrium optimizer, aquilla optimizer, and Honey Badger algorithm, to evaluate its performance superiority. The results achieved using the proposed approach have been superior or analogous to the erstwhile algorithms for performance comparability. Friedman's mean rank test is used to check the statistical significance of the propounded approach. The lowest Friedman's mean rank value obtained using the proposed approach indicates that the proposed approach has the potential to become an alternative to other well‐known strategies.