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Frankenstein Classifiers: Some experiments on the Sisyphus data set

H. Blockeel and J. Struyf. In Christophe Giraud-Carrier, Nada Lavrac, and Steve Moyle, editors, ECML/PKDD'01 workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, pages 1--12. ECML/PKDD'01 workshop notes, September 2001. More behind this link.

Abstract

We present some empirical results on the use of two methods for integrating different classifiers into a hybrid classifier that should perform better than each of its constituent classifiers. The main point of these methods is that instead of combining full classifiers, they combine pieces of them. One of the methods is based on ROC analysis; the other is based on augmenting the data with features derived from earlier learned classifiers. Experimental results are presented that suggest that these approaches can yield improvements over combining full classifiers.

BibTeX entry.

Frankenstein Classifiers: Some experiments on the Sisyphus data set

Abstract

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