Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

Abstract

Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features. Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression). As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space. However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem. In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed. The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm. Moreover, the k-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

“…Optimization algorithms have been applied successfully for FS in many applications such as data mining [27] using Particle Swarm Optimization, pattern recognition [28] using Binary Genetic Swarm Optimization, Medical applications [5] using Crow Search Optimization, and image analysis [29] using Genetic Algorithm Optimization, image processing [30], [31], [32] using Optimized Deep Neural Network, and there are many more. Nowadays, FS is an essential step to preprocess high-dimensional datasets.…”

“…Nowadays, FS is an essential step to preprocess high-dimensional datasets. It must be pointed that there are representative computational intelligence algorithms that have been applied to improve the FS in different studies such as [7], [9], [33], [34], [27], [46], and [47]. The optimization methods aim to obtain the optimal solution for FS (i.e., significant feature subset) within an appropriate time and cost.…”

Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

“…The authors of [27] considered an algorithm for feature selection based on PSO with learning memory (PSO-LM). Compared to the approach from this article, that approach introduces a memory learning strategy with the objective to balance the global exploration and the local exploitation in the algorithm.…”

Dynamic Butterfly Optimization Algorithm for Feature Selection

“…In the literature, there are several studies that applied optimisation algorithms for feature selection problem. For example, [3,4] improved whale optimisation algorithm (WOA) and used it for feature selection, [5] improved particle swarm optimisation (PSO) and used it for feature selection, [6] used Grasshopper optimisation algorithm (GOA) for feature selection, [7] used Firefly algorithm (FFA) for feature selection, [8] used Differential Evolution (DE) for feature selection, [1] improved Salp Swarm Algorithm (SSA)and used it for feature selection, [9] used Genetic Algorithm (GA) for feature selection, [10] used Grey Wolf Optimization (GWO) for feature selection, [11] improved Gravitational Search algorithm (GSA) and used it for feature selection, [12] used fish swarm optimisation (FSO) for feature selection, [13] improved crow search algorithm (CSA) and used it for feature selection, [14] improved Dragonfly Algorithm (DA) and used it for feature selection, [15] improved social spider algorithm (SSA) and used it for feature selection, [1,16] improved Salp Swarm Algorithm (SSA) and used it for feature selection, [2] improved Harris hawks optimisation (HHO) and used it for feature selection.…”