Multi-objective genetic algorithms as an effective tool for feature selection in the speech-based emotion recognition problem

Feature selection is a quite important step in data analysis. Extracting relevant attributes may not only decrease the dimensionality of the dataset and, consequently, reduce time costs spent on the next stages, but also contribute to the quality of the final solution. In this paper we demonstrate some positive effects of the usage of a heuristic feature selection scheme which is based on a two-criterion optimization model. The approach proposed is applied to the speech-based emotion recognition problem, which is currently one of the most important issues in human-machine interactions. A number of high-dimensional multilingual (English, German, Japanese) databases are involved to investigate the effectiveness of the technique presented. Three different multi-objective genetic algorithms and their cooperative modifications are applied as optimizers in combination with classification models such as a Multilayer Perceptron, a Support Vector Machine and Logistic Regression. In most cases we may observe not only a dimensionality reduction, but also an improvement in the recognition quality. To avoid choosing the most effective multi-objective genetic algorithm and the best classifier, we suggest applying a heterogeneous genetic algorithm based on several heuristics and an ensemble of diverse classification models.