Classification of Parkinson disease using binary Rao optimization algorithms

Rao algorithms are recently proposed optimization algorithms used to solve optimization problems. These algorithms are based on the best and the worst solutions, which are computed during the optimization process. However, these algorithms apply to continuous problems only. In this article, the binary versions of Rao algorithms are proposed, which can be used for solving feature selection problems. These are applied to four publicly available Parkinson's disease datasets. Besides providing an optimal set of features, the k parameter of the k-nearest neighbour classifier is also optimized by the proposed approach. The performance of these algorithms has been measured taking an average of 30 independent runs using a 10-fold cross-validation procedure. Also, a comparison of the performance has been made with the other state of the art methods. Significance analysis of these algorithms has been made with the Friedman rank test.

Automated summary

The binary versions of Rao algorithms are proposed for solving feature selection problems in Parkinson's disease datasets, optimizing the k parameter of the k-nearest neighbour classifier. The performance of these algorithms is evaluated through 30 independent runs with a 10-fold cross-validation procedure and compared with state of the art methods, with significance analysis conducted using the Friedman rank test.