Authors:
Christina Brester
1
;
Eugene Semenkin
1
;
Maxim Sidorov
2
and
Olga Semenkina
1
Affiliations:
1
Siberian State Aerospace University, Russian Federation
;
2
University of Ulm, Germany
Keyword(s):
Neural Network, Multicriteria Design, Cooperative Genetic Algorithm, Speech-based Emotion Recognition, Feature Selection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Human-Machine Interfaces
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Problems in Signal Processing
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
System Modeling
Abstract:
In this paper we introduce the two-criterion optimization model to design multilayer perceptrons taking into account two objectives, which are the classification accuracy and computational complexity. Using this technique, it is possible to simplify the structure of neural network classifiers and at the same time to keep high classification accuracy. The main benefits of the approach proposed are related to the automatic choice of activation functions, the possibility of generating the ensemble of classifiers, and the embedded feature selection procedure. The cooperative multi-objective genetic algorithm is used as an optimizer to determine the Pareto set approximation in the two-criterion problem. The effectiveness of this approach is investigated on the speech-based emotion recognition problem. According to the results obtained, the usage of the proposed technique might lead to the generation of classifiers comprised by fewer neurons in the input and hidden layers, in contrast to c
onventional models, and to an increase in the emotion recognition accuracy by up to a 4.25% relative improvement due to the application of the ensemble of classifiers.
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