Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

Ali Hakem Jabor,Ali Hussein Ali

doi:10.29196/jubpas.v27i1.2106

Abstract

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization).

Highlights

Over the last decades the devices, sensors, and users are on increasing, the dimensions of the datasets are increasing
The stagnation phenomena increase as search progress due to new candidate solutions are convergence
The Binary Particle Swarm Optimization (BPSO) suffering some time from stagnation at a local optimum

Summary

Introduction

Over the last decades the devices, sensors, and users are on increasing, the dimensions of the datasets are increasing. Increasing the number of mutation genes and decreasing the crossover operations during search progress help the proposed algorithm to be more robust to deal with stagnation problem. We focus on features selected based on a combination of two population metaheuristic techniques: Genetic algorithm (GA) and Binary Particle Swarm Optimization (BPSO). The proposed algorithm reduces the stagnation in BPSO by calling GA to decrease convergence in newly candidate solutions (population). The proposed method consists of four parts: binary metaheuristics optimization algorithms (BPSO and GA), feature selection, switching between algorithms, and termination. The PSO when going through the stagnation as progress search in several steps (θ) the proposed algorithm called GA to makes diversity in population (candidate solutions), recall the BPSO when GA failed to make diversity in candidate solutions. The same above function used in comparative and same parameters of CEC’15 that set (500 iterations, 20 x100 population, and average 30 runtimes.)

STD of BPSO

Ozone level detection

Findings

I.Conclusion and Future works