# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 1 Introduction
[8] introduced Attention-based Feature Selection (AFS), a novel neural-network-based feature selection architecture that achieves the best accuracy and stability compared to several state-of-the-art feature selection algorithms on various datasets, including those with small samples. [PERSON] et al. [9] proposed a feature selection method based on mutual information, called minimal-redundancy-maximal-relevance criterion (mRMR), which selects a compact set of superior features at low cost and leads to promising improvements in feature selection and classification accuracy. [PERSON] et al. [10] presented a kernel-based feature selection method that employs measures of independence to find the subset of covariates that is maximally predictive of the response. However, the conventional approaches are prone to failure against new challenges of the complex and present developing world. Newer models require a more flexible and efficient algorithm, and this is the domain where metaheuristic algorithms have been appreciated by the ML researchers. These optimization algorithms offer superiority based on ease and versatility, and they mainly are one of four types, i.e., evolution-based swarm intelligence, human-based and physics-based algorithms. The evolution-based algorithms include differential evolution (DE) [11] and the genetic algorithm (GA) [12] which have roots in the principles of natural selection, growth, mutation, etc. The swarm intelligence algorithms include a vast array of techniques inspired by colonies and swarms of natural species in the world and are constructed in similar ways of knowledge sharing and collaboration. They are further divided into sub-categories, such as insect (ant, bee, butterfly, dragonfly, firefly, glow-worm, etc.) and animal (lion, wolf, bat, monkey, emperor penguin, whale, hawk, etc.). Particle swarm optimization (PSO) is perhaps the most popular swarm intelligence algorithm [13]. Human-behavior-based algorithms include cultural evolution and teaching-learning-based optimization algorithms. Lastly, inspired by the laws of physics, physics-based algorithms are developed upon various phenomena, such as gravitational search and atom search optimization algorithms. In essence, there is no 'one size fits all' in complicated ML applications, and a schematic must be designed to select several of these techniques to fit the current problem [14]. This can lead to the creation of newer methods as a blend of these techniques or research of different phenomena in pursuit of a better model [15]. To provide a solution for better feature selection, several research efforts involving metaheuristics have generated commendable propositions. There is a plethora of literature available on the applications of metaheuristics to deal with feature selection problems. However, in context of the present study, the literature search is restricted to those algorithms considered in this paper. Thus, only the most recent and relevant literature on DE, GA, PSO, flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators' algorithm (MPA) and manta ray foraging optimization (MRFO) is included in this paper. [PERSON] and [PERSON] [16] presented a DE solution to analyze a brushless DC motor for faults with an advanced feature selection approach based on a distance discriminant system for categorizing similarities. The features were subsequently classified using a back-propagation neural network and linear discriminant analysis and were later optimized using the DE algorithm. [PERSON] et al. [17] proposed a new filter feature selection criterion that would not rely on mutual redundancy and would rank features by their proposed Relieff and Fisher scores combined with weightage to a classification task's class and a feature's mutual relevance. It would then lead to two new DE-based filter approaches discussed in their paper as a breakthrough. [PERSON] et al.