No abstract

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

Abstract. Alzheimer disease becomes a major issue in elder population. To enhance their autonomy and solve future financial issues, we propose an adaptive game based on a non-intrusive shoe-mounted accelerometer able to recognize daily activities and errors. By solving puzzles, the user can train himself his erroneous activities, and re-learn a few. The main goal behind this game is to delay the cognitive decline while creating an evaluation tool for the health-care professional by the computation of a score taking into account the correctitude and the perseverance.