Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model

Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.

Automated summary

Feature selection methods are essential for developing intelligent analysis tools that require data preprocessing and improving the performance of machine learning algorithms. This paper introduces a new feature selection method based on the modified Slime mould algorithm using the firefly algorithm. Experimental results confirm the promising performance of the method across different performance measures.