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.
CITATION STYLE
Letourneau, S., Matwin, S., & Famili, F. (1998). A normalization method for contextual data: Experience from a large-scale application. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1398, pp. 49–54). Springer Verlag. https://doi.org/10.1007/bfb0026671
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