English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 conventional 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.

Multicriteria Neural Network Design in the Speech-based Emotion Recognition Problem