Model Selection for Support Vector Machines Using Ant Colony Optimization in an Electronic Nose Application

Abstract

Abstract. Support vector machines, especially when using radial basis kernels, have given good results in the classification of different volatile compounds. We can achieve a feature extraction method adjusting the parameters of a modified radial basis kernel, giving more importance to those features that are important for classification proposes. However, the function that has to be minimized to find the best scaling factors is not derivable and has multiple local minima. In this work we propose to adapt the ideas of the ant colony optimization method to find an optimal value of the kernel parameters.

Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

“…The ensemble SVM was also optimized with Ant Colony Optimization (Jinyu andXin, 2009 andAcevedo et al, 2006)) to select the parameters of SVM automatically and that proved to be effective. Ant Colony Optimization (ACO) is a metaheuristic algorithm that uses the idea exhibited in an ant colony to find the shorted path from a food source to the nest through pheromone information without employing any visual cues.…”

Ajuste de parâmetros de técnicas de classificação por algoritmos bioinspirados

“…Diversos trabalhos encontrados na literatura tratam do problema de ajuste de parâmetros para SVMs (Lorena & Carvalho, 2006;Huang & Wang, 2006;Souza & Carvalho, 2005;Souza et al, 2006;Imbault & Lebart, 2004;Zhang & Jiao, 2005;Acevedo et al, 2006) e para RNs (Castillo et al, 2007;Gao et al, 2006;Braun & Weisbrod, 1993;Dodd, 1990;Leung et al, 2003;Tsai et al, 2006). Muitos deles utilizam algoritmos bioinspirados para isso.…”