A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

Abstract

Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

“…To improve our algorithm, we have done more in-depth research on PSO [26][27][28]. In [26], to solve the many-objective optimization problem, the author proposed a binary particle swarm optimization with a two-level particle cooperation strategy.…”

“…In [27], an improved localized FS approach based on multi-objective binary PSO was proposed, it addressed fault diagnosis from a novel perspective that takes advantage of the local distribution of data without balancing strategies. In [28], a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and PSO was proposed to search for the feature subsets with and without entropy-based cut points simultaneously. Enlightened by these works, the HPSO is proposed here based on the standard PSO.…”

Discarding–Recovering and Co-Evolution Mechanisms Based Evolutionary Algorithm for Hyperspectral Feature Selection

“…Zhao et al [32] proposed a multiple populations co-evolution mechanism and multi-stage interaction learning (OL) mechanism to fully search the prospective features in the stagnant state and increase the possibility of jumping out of local optima. Zhou et al [33] proposed a feature selection method based on a cooperative co-evolution mechanism (CC-DFS). This method used a heterogeneous model to search for feature combinations with cut-off points and feature combinations without cut-off points, resulting in improved performance and generalization ability.…”

“…To verify the search efficiency of the co-evolution mechanism in the proposed method, it is compared with other co-evolution mechanisms named CC-DFS [33] and CC-RFG [34]; the average fitness value of each iteration on three HSI datasets is shown in Figure 8. The experimental results indicate that the increasing size of the training set from 5% to 10% leads to a significant improvement in the OA.…”

Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data

“…Most of the feature selection algorithms proposed in the literature are classification-based techniques [10,11] and [12], where these methods are dependent on the presence of clear classes or labels to perform the feature selection accordingly, or a clear presence of heuristic information generated by various algorithms such as, genetic algorithms as in [13].…”