Abstract
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.
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Kubat, M., Holte, R. C., Matwin, S.: Machine Learning for the Detection of Oil Spills in Satellite radar Images Machine Learning Journal, Special Issue on Applications of ML. (1998) to appear.
Letourneau, S., Matwin, S., Famili, F.: A Normalization Method for Contextual Data: Experience From A Large-Scale Application. TR-98-02, www.csi.uottawa.ca/≈stan/public_html/techrep/TR-98-02.ps.
Taylor, C., Nakhaeizadeh, G.: Learning in Dynamically Changing Domains: Theory Revision and Context Dependence Issues. Proceedings of ECML-97. (1997) 353–360.
TURNEY, P, Halasz, M: Contextual Normalization Applied to Aircraft Gas Turbine Engine Diagnosis. Journal of Applied Intelligence 3 (1993) 109–129
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© 1998 Springer-Verlag Berlin Heidelberg
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Létourneaue, S., Matwin, S., Familie, F. (1998). A normalization method for contextual data: Experience from a large-scale application. In: Nédellec, C., Rouveirol, C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026671
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DOI: https://doi.org/10.1007/BFb0026671
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