Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

Abstract

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.

A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

“…Common heuristic optimization algorithms include genetic algorithm (GA) [1], simulated annealing [2], crow search algorithm [3], ant colony optimization [4], differential evolution (DE) [5], particle swarm optimization (PSO) [6], bat algorithm (BA) [7], cuckoo search algorithm (CSA) [8], whale optimization algorithm (WOA) [9], firefly algorithm (FA) [10], grey wolf optimizer (GWO) [11], teaching-learning-based optimization [12], artificial bee colony (ABC) [13], and chimp optimization algorithm (ChOA) [14]. As technology continues to evolve and update, the heuristic optimization algorithms are currently widely applied in various areas of real life, such as the welded beam problem [15], feature selection [16][17][18], the welding shop scheduling problem [19], economic dispatch problem [20], training neural networks [21], path planning [15,22], churn prediction [23], image segmentation [24], 3D reconstruction of porous media [25], bankruptcy prediction [26], tuning of fuzzy control systems [27][28][29], interconnected multi-machine power system stabilizer [30], power systems [31,32], large scale unit commitment problem [33], combined economic and emission dispatch problem [34], multi-robot exploration [35], training multi-layer perceptron [36], parameter estimation of photovoltaic cells [37], and resource allo...…”

Search Graph Magnification in Rapid Mixing of Markov Chains Associated with the Local Search-Based Metaheuristics

“…Metaheuristics are applied successfully to obtain the optimum or near to optimum solution for many optimization problems in the literature [31][32][33][34][35][36][37][38]. To apply metaheuristics for the optimization problem, one should define a search space for the problem.…”