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Feature Engineering for a Gene Regulation Prediction Task

Forman, George

HPL-2002-318

Keyword(s): machine learning; hierarchical attributes; bioinformatics; KDD Cup Competition

Abstract: This paper describes an approach that won honorable mention for the gene regulation prediction task of the 2002 KDD Cup competition [1]. Our methodology used extensive cross-validation to direct the search for an appropriate problem representation and the selection of an 'off-the-shelf' induction algorithm. A prominent trait of the dataset is the presence of three hierarchical attributes, for each of which we generated a novel predictive feature: the percentage of positives hierarchically aggregated at the node specified by the instance. Notes: Published in KDD Explorations, 4(2), 2003

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