Regular Papers Applications of ML

Machine Learning: ECML-98

Volume 1398 of the series Lecture Notes in Computer Science pp 49-54


A normalization method for contextual data: Experience from a large-scale application

  • Sylvain LétourneaueAffiliated withIntegrated Reasoning Group, National Research Council of Canada
  • , Stan MatwinAffiliated withSchool of Information Technology and Engineering, University of Ottawa
  • , Fazel FamilieAffiliated withIntegrated Reasoning Group, National Research Council of Canada

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This paper describes a pre-processing technique to normalize contextually-dependent data before applying Machine Learning algorithms. Unlike many previous methods, our approach to normalization does not assume that the learning task is a classification task. We propose a data pre-processing algorithm which modifies the relevant attributes so that the effects of the contextual attributes on the relevant attributes are cancelled. These effects are modeled using a novel approach, based on the analysis of variance of the contextual attributes. The method is applied on a massive data repository in the area of aircraft maintenance.


Learning in contextual domains attribute normalization datamining