BAHA: Binary artificial hummingbird algorithm for feature selection

Datasets classification accuracy depends on their features. The presence of irrelevant and redundant features in the dataset leads to the reduction of classification accuracy. Identifying and removing such features is the main purpose in feature selection, which is an important step in the data science lifecycle. The objective of the Wrapper feature selection method is to reduce the number of selected feature (NSF) while improving the classification accuracy by working on a set of features. The feature selection is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally cheap and efficient algorithm to solve it. The artificial hummingbird algorithm (AHA) is a biological inspired optimization technique that mimics the unique flight capabilities and intelligent foraging tactics of hummingbirds in nature. Since feature selection is inherently a binary problem. In this paper, the binary form of the AHA meta-heuristic algorithm is proposed to show that binarizing the AHA meta-heuristic algorithm improves its performance for solving feature selection problems. The proposed method is tested on a standard benchmark dataset and compared with four state-of-the-art feature selection algorithms: Automata-based improved equilibrium optimizer with U-shaped transfer function (AIEOU), Whale optimization approaches for wrapper feature selection (WOA-CM), Ring theory-based harmony search (RTHS), and Adaptive switching gray-whale optimizer (ASGW). The results show the effectiveness of the proposed algorithm in searching for optimal features subset. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/baha.