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%.
Highlights
Gestures are composed of multiple body-part motions and can form activities [1].gesture recognition offers a wide range of applications, including inter alia, fitness training, human robot and computer interaction, security, and sign language recognition.Likewise, gesture recognition is employed in ambient assisted living systems for tackling burgeoning and worrying public healthcare problems, such as autonomous living for people with dementia and Parkinson’s disease
We propose a many-objective formulation to simultaneously deal with optimal feature subset selection, discretization, and parameter tuning for an LM-WLCSS classifier
The validation of our simultaneous feature selection, discretization, and parameter tuning for LM-WLCSS classifiers is carried out
Summary
Gestures are composed of multiple body-part motions and can form activities [1].gesture recognition offers a wide range of applications, including inter alia, fitness training, human robot and computer interaction, security, and sign language recognition.Likewise, gesture recognition is employed in ambient assisted living systems for tackling burgeoning and worrying public healthcare problems, such as autonomous living for people with dementia and Parkinson’s disease. Gestures are composed of multiple body-part motions and can form activities [1]. Gesture recognition offers a wide range of applications, including inter alia, fitness training, human robot and computer interaction, security, and sign language recognition. A large amount of work has been conducted on image-based sensing technology, camera and depth sensors are limited to the environment in which they are installed. They are sensitive to obstructions in the field of vision, variation in luminous intensity, reflection, etc. Wearable sensors and mobile devices are more suitable for monitoring ambulatory activities and physiological signals
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