Discovering Relevant Sensor Data by Q-Analysis

Abstract. This paper proposes a novel method for supervised classification based on the methodology of Q-analysis. The classification is based on finding ‘relevant’ structures in the features describing the data, and using them to define each of the classes. The features not included in the structural definition of a class are considered as ‘irrelevant’. The paper uses three different data-sets to experimentally validate the method.

• Type: Conference
• Year: 2005
• Where: ROBOCUP
• Authors: Pejman Iravani
• Keywords: Different Data-sets, ROBOCUP 2005, Structural Definition, Supervised Classification