Feature engineering for a gene regulation prediction task

Authors:
George Forman
Affiliations:
HP Labs, Palo Alto, CA
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2002

Citing 3
Cited 2

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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.