Parameter selection method for support vector machine based on hybrid bat algorithm

Abstract

The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and machine learning, and relates to an improvement in parameter selection of an intelligent discrimination method of a support vector machine (Support Vector Machine, SVM) model. Background technique [0002] Support Vector Machine (Support Vector Machine, SVM) is a classifier based on the structural risk minimization principle of computational learning theory and the VC dimension theory. Its main idea is to find an optimal hyperplane in the high-level space as the division of the two categories for the two-category classification problem, so as to ensure the minimum classification error rate. Compared with traditional classifiers that only use the principle of minimizing empirical risk as a model, SVM can effectively avoid over-fitting when dealing with small samples, and SVM has a good classification effect on linearly inseparable problems. After it came out, it has received extensive at...

AI Technical Summary

Problems solved by technology

[0005] The bat algorithm has the advantages of swarm intelligence algorithms, such as powerful global search range and short convergence time. Alaa Tharwat used it for SVM parameter selection and achieved good experimental results, but there are also some common shortcomings of swarm intelligence algorithms. , such as the premature phenomenon of the algorithm is prone to occur, and the solution accuracy cannot meet the target requirements, etc.

Images