ADHOC: a Tool for Performing Effective Feature Selection

Authors:
Marco Richeldi;Pier Luca Lanzi
Affiliations:
-;-
Venue:
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Year:
1996

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Abstract

This paper introduces ADHOC, a tool that integrates statistical methods and machine learning techniques to perform effective feature selection. Feature selection plays a central role in the data analysis process since redundant and irrelevant features often degrade the performance of induction algorithms, both in speed and predictive accuracy. ADHOC combines the advantages of both filter and feedback approaches to feature selection to enhance the understanding of the given data and increase the efficiency of the feature selection process. We report results of extensive experiments on real-world data which demonstrate the effectiveness of AD-HOC as data reduction technique as well as feature selection method. ADHOC has been employed in the analysis of several corporate databases. In particular, it is currently used to support the difficult task of early estimating the cost of software projects.