Probabilistic Multimodal Classification with dynamic feature selection

A novel Probabilistic,Multimodal Classification with Dynamic Input Selection (PMC-D) framework is proposed in this paper. In this approach, Probability Distribution Functions (PDF), which are determined for each i) modality's input feature and ii) output class, are used to score per class the importance of each given input feature value. Furthermore, these scores/beliefs, which are dependent on the instantaneous (dynamic) values of input features, are used to reduce classification dimensionality and increase classification performance. PMC-D is generic and does not require weighting or normalization of feature scores. Moreover, using simulated Gaussian and non-Gaussian PDF types of input datasets, as well as datasets provided from three well-known real applications, experimental results have shown that the PMC-D methodology offers classification advantages when compared to well-known classification techniques.