About the methods for selection informative features using self-adjusting neural network classifiers and their ensembles

Using feature selection procedures based on filters is useful on the pre-processing stage for solving the task of data analysis in different domains including an air-space industry. However, it is a complicated problem, due to the absence of class labels that would guide the search for relevant information. The feature selection using “wrapper” approach requires a learning algorithm (function) to evaluate the candidate feature subsets. However, they are usually performed separately from each other. In this paper, we propose two-stage methods which can be performed in supervised and unsupervised forms simultaneously based on a developed scheme using three criteria for estimation (“filter”) and multi-criteria genetic programming using self-adjusting neural network classifiers and their ensembles (“wrapper”). The proposed approach was compared with different methods for feature selection on tree audio corpora in German, English and Russian languages for the speaker emotion recognition. The obtained results showed that the developed technique for feature selection provides to increase accuracy for emotion recognition.