AN EFFICIENT MODEL SELECTION FOR SVM IN REAL-WORLD DATASETS USING BGA AND RGA

作者：Almasi Omid Naghash^*; Akhtarshenas Ehsan; Rouhani Modjtaba

来源：Neural Network World, 2014, 24(5): 501-520.

DOI：10.14311/nnw.2014.24.029

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

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.

• 出版日期2014