# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 1 Introduction  
With technology across all walks of life developing rapidly to expand the I/O bandwidth of intelligent systems to provide more utility, all these endless bit streams are becoming a hassle to manage. Storing heaps of data is a task that can still be managed easily; however, mining relevant data for the current intent and application has become increasingly daunting with the evolution of larger datasets. This is exactly where feature selection comes into play. Feature selection is the technique and process that aims to reduce the complexity and dimension of the existing dataset under observation by increasingly focusing on relevant subset tiers of information. This helps eliminate chunks of data considered redundant, i.e., repetitive, and data that are considered irrelevant, i.e., unrelated to the preferred feature/variable/attribute of choice. Feature selection has become particularly useful in machine learning (ML) applications since it can effectively reduce the database dimensions, thereby eliminating a lot of noise (useless filler data from the external environment which is not considered important to the current task) to make it highly optimized and easy to interpret by both the system and ML experts. This, in turn, would help inlowering the model training times, saving transmission bandwidths by a pre-processing screening of data inputs, reducing the burden on resources while raising efficiency and helping to safeguard against problems of dimensionality [1]. Conventional feature selection approaches are based on the wrapper, filter and embedded methods, with the preference of the method being dependent on the evaluation metrics under consideration and allowed error percentage bracket. The wrapper method is the most computationally intensive of them all, with the wrapper dividing the database into several feature subsets based on classes to train an ML model. It then scores the results based on the number of errors generated in the subset under evaluation, defined as the error rate of the subset. The subset with the least error rate is declared the most optimized one for the given problem and is hence chosen. The filter method reduces the complexity of operations undertaken by the wrapper, but at the expense of classification accuracy. A filter defines the score of a feature subset based on its relation to other features using the common information or traits. The subsets can then be sorted according to the desired attributes depending on the score or weight they have received from the system. In large-scale applications, a filter method usually acts as a pre-wrapper stage to improve the speed of the wrapper while reducing the dimensional scale of the input stream. Embedded methods often involve regression coefficients in their operation and carry out their feature selection stages as a part of the model generation process, and they are a blend of both wrappers and filters [2]. [PERSON] et al. [3] proposed the Deep Neural Pursuit (DNP) algorithm, which selects a subset of high-dimensional features to alleviate overfitting and uses multiple dropouts to calculate gradients with low variance. GRACES, developed by [PERSON] et al. [4], employs graph convolutional networks for feature selection. Both DNP and GRACES have been shown to outperform other methods in both synthetic and real-world biological datasets. [PERSON] et al. [5] proposed a Bayesian approach to mixture learning that simultaneously optimizes the number of components, feature saliency, and mixture model parameters. [PERSON] et al. [6] proposed Deep Feature Screening (DeepFS), a nonparametric approach that combines deep neural networks and feature screening for ultra-high-dimensional data with a small number of samples. [PERSON] et al. [7] presented a novel approach for high-dimensional feature selection using feature-wise kernelized Lasso, which efficiently captures nonlinear input-output dependencies. [PERSON] et al.