An efficient model selection for SVM in real-world datasets using BGA and RGA

Title:

An efficient model selection for SVM in real-world datasets using BGA and RGA

Creator:

Almasi , Omid Naghash, Akhtarshenas, Ehsan, and Rouhani, Modjtaba

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:97eb8c79-f88f-4ae3-9c3d-b56341ce4532
uuid:97eb8c79-f88f-4ae3-9c3d-b56341ce4532
doi:10.14311/NNW.2014.24.029

Subject:

Model selection, model complexity, support vector machines, genetic algorithms, classification, and real-world datasets

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Support vector machine (SVM) has become one of the most popular machine-learning methods during the last years. The design of an efficient model and the proper adjustment of the SVMs parameters are integral to reducing the testing time and enhancing performance. In this paper, a new bipartite objective function consisted of the sparseness property and generalization performance is proposed. Since the proposed objective function is based on selecting fewer numbers of the support vectors, the model complexity is reduced while the performance accuracy remains at an acceptable level. Due to the model complexity reduction, the testing time is decreased and the ability of SVM in practical applications is increased Moreover, to prove the performance of the proposed objective function, a comparative study was carried out on the proposed objective function and the conventional objective function, which is only based on the generalization performance, using the Binary Genetic Algorithm (BGA) and Real-valued vectors GA (RGA). The effectiveness of the proposed cost function is demonstrated based on the results of the comparative study on four real-world datasets of UCI database.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:5

Harvested from:

CDK

Metadata only:

false