Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition
Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition is a scholarly work, published in 2024 in ''Computers, Materials and Continua''. The main subjects of the publication include selection (genetic algorithm), random forest, Type-2 fuzzy sets and systems, artificial intelligence, Sort, Speech enhancement, multi-objective optimization, ranking function, feature, filter, speech recognition, emotion recognition, machine learning, pattern recognition, feature selection, and computer science. Speech emotion recognition (SER) uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high, so authors introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First, authors use the information gain and Fisher Score to sort the features extracted from signals.Then, authors employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally, authors propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection, which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers, four English speech emotion datasets are employed to test the proposed algorithm (MBEO) as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance, hypervolume, Pareto solutions, and execution time, and MBEO is appropriate for high-dimensional English SER.