A Discrete Moth Flame Algorithm for Feature Selection
International journal of intelligent engineering and systems · 2022
A crucial and important task in machine learning is feature selection (FS).The primary goal of the feature selection task is to minimize the dimension of the feature set while preserving performance accuracy.In order to address the FS task, a discrete moth flame algorithm that is combined with levy flights (DL-MFA) is presented in this research.The proposed DL-MFA imitates the natural navigational patterns of moths.The moths move along a straight line at a constant angle in the direction of the true light source (the moon) known as transverse orientation.Additionally, moths are drawn to artificial lights like fires and because of the close proximity; they constantly adjust their flying angles, creating a spiral path.In order to maintain healthy population diversity and increase the global search capabilities of the algorithm, the levy flight search technique is also used as a regulator of the moth position updating mechanism.The five swarm intelligence algorithms (SIAs) are contrasted with the proposed algorithm using measures such as entropy, purity, completeness score (CS), and homogeneity score (HS).For evaluating fitness, the SSE fitness function is utilised.The outcomes have shown that the proposed algorithm achieved purity values in the range 90% to 100%, and entropy 10% to 50%.Proposed DL-MFA has also achieved homogeneity score and completeness score up to 50%.These results prove that the proposed algorithm is better than its state-ofthe-art competitors.