Adaptive Feature Selection through Fisher Discriminant Ratio

We present an adaptive feature selection method that makes use of Fisher’s discriminant ratio (FDR) with flexible threshold which is adjusted in a person-specific manner. The proposed method is shown to improve the detection of high-arousal negative-valence (HANV) conditions, based on two combinations of physiological signals (ECG+GSR and PPG+GSR). We validate the proposed method in an experimental setup aiming at the automated detection of HANV conditions evoked by audio-visual stimuli and picture stimuli. The experimental results support that the proposed method yields to an improvement of the classification accuracy of an SVM-based detector on average with 5.6%±0.6% in comparison with the traditional non-adaptive FDR-based feature selection using threshold 0.3, and with the full set of 39 features.