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2 Journal of Artificial Intelligence and Soft Computing Research

2 2016

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Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection

Stanovov V., Semenkin E., Semenkina O.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 3

173--188

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

Multi-objective heuristic feature selection for speech-based multilingual emotion recognition

Brester C., Semenkin E., Sidorov M.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 4

243--253

If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).