Possibilistic reasoning effects on Hidden Markov Models effectiveness

Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficiency in stochastic sequences labeling, they are overwhelmed by imperfect quality of used data in the learning and inference processes. In this paper, we try to evaluate the contribution of possibilistic theory in creating sequences of observations used by HMM models. Experimental results show that observation sequences, obtained by possibilistic reasoning significantly, improve the performance of HMM in the recognition of online e-learning activities.