A Feature Selection Approach to the Group Behavior Recognition Issue Using Static Context Information

Abstract

This paper deals with the problem of group behavior recognition. Our approach is to merge all the possible features of group behavior (individuals, groups, relationships between individuals, relationships between groups, etc.) with static context information relating to particular domains. All this information is represented as a set of features by classification algorithms. This is a very highdimensional problem, with which classification algorithms are unable cope. For this reason, this paper also presents four feature selection alternatives: two wrappers and two filters. We present and compare the results of each method in the basketball domain.

Medical Data Processing and Analysis for Remote Health and Activities Monitoring

“…These are procedural and algorithmic methods for discovering relationships among concepts of interest that being captured by the system (sensor data and contextual information). There is a tradeoff involved in trying to develop fully-automated algorithmic DF processes for complex problems where the insertion of human intelligence at some point in the process may be a much more judicious choice [23].…”