Abstract
In this paper, we introduce the use of combinations of heterogeneous classifiers to achieve better diversity. Conducting theoretical and empirical analyses of the diversity of combinations of heterogeneous classifiers, we study the relationship between heterogeneity and diversity. On the one hand, the theoretical analysis serves as a foundation for employing heterogeneous classifiers in Multi-Classifier Systems or ensembles. On the other hand, experimental results provide empirical evidence. We consider synthetic as well as real data sets, utilize classification algorithms that are essentially different, and employ various popular diversity measures for evaluation. Two interesting observations will contribute to the future design of Multi-Classifier Systems and ensemble techniques. First, the diversity among heterogeneous classifiers is higher than that among homogeneous ones, and hence using heterogeneous classifiers to construct classifier combinations would increase the diversity. Second, the heterogeneity primarily results from different classification algorithms rather than the same algorithm with different parameters.
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Hsu, KW., Srivastava, J. (2009). Diversity in Combinations of Heterogeneous Classifiers. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_97
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DOI: https://doi.org/10.1007/978-3-642-01307-2_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01306-5
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