# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 1 Introduction
[18] came up with a multi-objective DE-based solution to feature selection while tackling the issues with three embedded operators into a new approach, called binary differential evolution with self-learning. [PERSON] and [PERSON] [19] presented a GA-based function built on a support vector machine (SVM) to diagnose heart disease in an optimized manner compared to the existing techniques. In addition, [PERSON] et al. [20] developed a nested GA-based technique useful to select features in a cancer microarray. The data were first fed into a \(t\)-test as a pre-processing stage to a dual-nested GA (outer and inner stages), and the proposed technique had passed five-fold cross-validation for a colon cancer data trial. [PERSON] et al. [21] paired GA with PSO to develop a hybrid algorithm with higher search capabilities and better efficiency in feature selection. [PERSON] et al. [22] endeavored to integrate Fast Fourier transform with GA and PSO to deal with applications of functional magnetic resonance imaging used to select features from high-dimensional images of brain scans. Using particle swarms to emulate bird and fish swarm behavior has also been an interesting concept to integrate into ML to search for candidate solutions. [PERSON] et al. [23] successfully utilized PSO in conjunction with naive Bayes, K-nearest neighbor (KNN) and fast decision tree learner classifiers for accurate breast cancer recurrence prediction. [PERSON] [24] combined PSO with the gray wolf optimizer (GWO), firefly algorithm (FFA) and GA for feature selection in a network intrusion detection system to monitor erratic network traffic. The proposed approach could employ both wrappers and filters designed for the considered algorithms and select features with higher accuracies. [PERSON] et al. [25] suggested the application of PSO to identify the type and dimension of particles suspended in transformer oil with the aid of partial discharge patterns. For generalized feature selection, [PERSON] and [PERSON] [26] integrated PSO with FPA to propose a hybrid binary solution, called BPSOFPA. The algorithm had both the S-shaped and V-shaped transfer functions, with the V-shaped function being highly successful in enhancing the selection performance of PSO and FPA. [PERSON] and [PERSON] [27] also formulated an improved FPA by blending it into the AdaBoost algorithm to demonstrate feature selection solutions in classifying text documents and observed that the hybrid approach would have higher detection accuracy than a standalone AdaBoost. A marine predator's relationship with its prey can also form the basis of an algorithm's design for global searches. [PERSON] et al. [28] derived a fractional-order comprehensive learning MPA, having its foundation on a standard MPA, while fixing the lack of iterations inherent in the algorithm with the addition of fractional calculus. [PERSON] et al. [29] developed a modified MPA to analyze COVID-19 X-ray images using fractional-order calculus techniques hybridized with MPA to improve accuracy against the existing convolutional neural network (CNN) technique. Proposing a different add-on to MPA, [PERSON] et al. [30] suggested the use of KNN to form a hybrid MPA-KNN for efficient feature selection purposes, with the trial being on medical datasets. Another attempt involving a different marine creature includes the work of [PERSON] et al. [31], dealing with an improved binary version of the MRFO. The proposed approach would help in solving binary problems and network intrusion detection issues by a random forest classifier. [PERSON] et al. [32] compared the effectiveness and efficiency of S-shaped and V-shaped functions for binary MRFO for feature selection. Some researchers have also been interested in mutualism-based cooperative interactions between organisms, and [PERSON] and [PERSON] [33] proposed an efficient binary chaotic SOS algorithm for solving feature selection problems.