Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Abstract

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…It was originally developed to remove two major problems of local optima trapping and slow convergence in high-dimensional datasets. After it was developed, Wu et al [ 256 ] proposed an enhanced version of the ChOA called enhanced chimp optimization algorithm (EChOA) to solve feature selection problems. This study discovered that despite the four divisions of the hunting strategy baseline algorithm to diversify the population, the algorithm was still limited by local optimum trapping, which led to the EChOA’s introduction of highly disruptive polynomial mutation (HDPM) for the population diversity increment.…”

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

“…The chimps are upgraded their places dependent upon the other chimps, and this mathematical process is signified in Eqs. ( 16) and (18).…”

“…where f non-linearly decayed in 2.5 to 0; r 1 and r 2 refers the arbitrary number amongst zero and one, and m has the chaotic vector. The dynamic co-efficient f is chosen to distinct curves and slopes, so the chimps are utilized various capabilities for searching the prey [18].…”

Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

“…Since the birth of these datasets, machine-learning and later deep-learning techniques have been massively applied in the study of network attack traffic detection models. Currently, the primary methods applied in machine learning for network traffic attack detection include support vector machines (SVM) [12], decision trees [13], Bayesian [14] and artificial neural networks [15], etc. The NSLKDD dataset cannot meet the current research needs in the field of intrusion detection due to its inherent shortcomings.…”