A hyper‐solution framework for classification problems via metaheuristic approaches

This is a summary of the author’s PhD thesis, supervised by Prof. Domenico Conforti and defended on 26‐02‐2010 at the Universitá della Calabria, Cosenza. The thesis is written in Italian and a copy is available from the author upon request. This work deals with the development of a high‐level classification framework which combines parameters optimization of a single classifier with classifiers ensemble optimization, through meta‐heuristics. Support Vector Machines (SVM) is used for learning while the meta‐heuristics adopted and compared are Genetic‐Algorithms (GA), Tabu‐Search (TS) and Ant Colony Optimization (ACO). Single SVM optimization usually concerns two approaches: searching for optimal set up of a SVM with fixed kernel (Model Selection) or with linear combination of basic kernels (Multiple Kernel Learning), both issues were considered. Meta‐heuristics were used in order to avoid time consuming grid‐approach for testing several classifiers configurations and some ad‐hoc variations to GA were proposed. Finally, different frameworks were developed and then tested on 8 datasets providing reliable solutions.