Hybrid slime mould - Grey wolf optimization algorithm for efficient feature selection

Abstract

Selection of features is an effective method for minimizing the amount of data features in order to improve machine learning classification performance. Choosing a set of attributes is a high-level procedure for selecting a collection of relevant features. To boost the classifier's performance, use a dimensional dataset. We outline a typical feature selection problem in this work in order to minimize the amount of role and responsibilities while improving accuracy. Different classification dataset from the Machine learning repository have been used to test SMA with GWO as a feature selection strategy. The feature selections for UCI repository datasets include Bat optimization, Cuckoo search optimization, Slime mould optimization, Whale optimization, Particle swarm optimization, and Grey wolf optimization.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…4.2.9. Others Moreover, researchers have hybridized an SMA with a sine cosine algorithm [83], marine predators algorithm [85], particle swarm optimization [97], evolutionary algorithm [98], firefly algorithm [99], gray wolf optimization algorithm [100], gradient-based optimizer [101], quadratic approximation [102], tournament selection [103], artificial neural network [104], moth-flame optimization algorithm [105], pattern search algorithm [106], and support vector regression [107]. These hybrid SMA variants indicated their benefits, such as the good balance between exploration and exploitation capabilities, good convergence speed, ability to avoid premature convergence, and reduced computation time.…”

“…Anji reddy Vaka et al [91] proposed a hybrid WOA-SMA and applied it to BreakHis and IDC datasets to evaluate breast cancer classifications. Khan AA et al [100] presented a hybrid of an SMA with GWO for feature selection purpose and evaluated it in UCI repository datasets by comparing it to other algorithms. Ewees AA et al [101] also evaluated the performance of the proposed GBOSMA in several benchmark datasets to solve feature selection problems, which showed that the GBOSMA superseded the other models.…”

Advances in Slime Mould Algorithm: A comprehensive Survey

“…Sayed GI et al [154] introduced a pistachio species classification method on the basis of SMA. Khan AA et al [156]presented a hybrid of SMA with GWO for feature selection and evaluated it in UCI repository datasets by comparing with other algorithms. Wei X et al [157] proposed a SMA-VMD-WTD model to identify and eliminate the transient electromagnetic signal noise.…”