Genetic Multiobjective Optimisation with Elite Insertion for EEG Feature Selection Embedded Feature Selection (FS) ensures the selection of few, relevant features, by directly redesigning the classifier for subsets of features. Naturally, this problem is formulated as a multiobjective optimization (MOO) addressing to the accuracy of the classifier and the parsimony of the feature vector. In MOOs, common ranking techniques use dominance analysis for providing a partial sorting of the solutions. Unfortunately, dominance analysis can also promote solutions less useful for the application. In order to gradually guide the search towards a userpreferred area set around the middle of the best fronts, this paper proposes an adaptive ranking algorithm with insertion of elites (ARE), which could be integrated in any MOO genetic algorithm. ARE incorporates two new procedures proposed for labeling the preferred solutions and for inserting elites in the less populated areas, whenever a biased exploration is detected. The experimental investigations illustrate that GA with ARE offers better results than NSGAII, both for electroencephalogram (EEG) feature selection problem (which likely involves weakly conflicting objectives) and MOOs with strongly conflicting objectives.

Extracting AudioVisual Features for Emotion Recognition Through Active Feature Selection Research in automatic emotion recognition has seldom addressed the issue of computational resource utilisation. With the advent of ambient technology, which employs a variety of lowpower, resource constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions aim at maintaining the userxe2x80x99s independence as unobtrusively as possible. In this context, efforts are being made to model human social signals such as affects using lowcost technologies, which can aid health monitoring. This paper presents an Active Feature Selection (AFS) method using selforganized maps neural networks for emotion recognition in the wild. The AFS is used for feature subsets selection from three different feature sets: 62 out of 88 features were selected for eGeMAPs, 21 out of 988 for emobase, and 140 out of 2832 for LBPTOP features. The results show that the features subsets selected by AFS provide better results than the entire feature set and PCA dimensionality reduction method. The best improvement is observed on emobase features, followed by eGeMAPs. For visual features, nearly the same results are obtained with a significant reduction in dimensionality (only 5% of the full feature set is required for the same level of accuracy). The weighted score fusion results in an improvement, leading to 43.40% and 40.12% accuracies on the EmotiW 2018 validation and test datasets respectively.