Combining Multi Classifiers Based on a Genetic Algorithm - A Gaussian Mixture Model Framework

Combining outputs from different classifiers to achieve high accuracy in classification task is one of the most active research areas in ensemble method. Although many state-of-art approaches have been introduced, no method is outstanding compared with the others on numerous data sources. With the aim of introducing an effective classification model, we propose a Gaussian Mixture Model (GMM) based method that combines outputs of base classifiers (called meta-data or Level1 data) resulted from Stacking Algorithm. We further apply Genetic Algorithm (GA) to that data as feature selection strategy to explore an optimal subset of Level1 in which our GMM-based approach can achieve high accuracy. Two methods are combined in a single framework called GAGMM. Experiments implemented on 21 UCI Machine Learning Repository data files and CLEF2009 medical image database demonstrate the advantage of our framework compared with other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules as well as GMM-based approaches on original data.