An enhanced Harris hawk optimizer based on extreme learning machine for feature selection

The growth of data creates more analysis and mining challenges related to speed and accuracy. Feature selection (FS) is an optimization problem used as a preprocessing phase to reduce the data dimensionality while obtaining the best classification accuracy. FS removes redundant and irrelevant features and preserves the best informative features. Various meta-heuristic optimization algorithms were employed in the literature to solve the FS problem. This paper proposes an improved Harris hawk optimization algorithm called (IHHO) to find the optimal feature set for classification purposes in a wrapper-based environment. Three main improvements are obtained in the binary version of HHO. The first improvement is to speed up the convergence, which is implemented using the most informative features in population initialization. Both filter-based and wrapper-based techniques are used during the initialization phase. The second one is to ensure the global and local search and avoid trapping into local optima using the X-shaped transfer function. While the third one is using the extreme learning machine as the base classifier to guide the searching process, speed up the convergence, and improve the accuracy of the FS process. The proposed model was evaluated using 18 well-known UCI benchmarks and compared with traditional HHO, particle swarm optimization, gray wolf optimizer, grasshopper optimization algorithm, and five standard filter-based techniques. The experiment results prove the superior performance of the IHHO compared to other algorithms and methods presented in the literature.