Abstract
Decision tree learning is relatively non-robust: a small change in the training set may significantly change the structure of the induced decision tree. This paper presents a decision tree construction method in which the domain model is constructed by consensus clustering of N decision trees induced in N-fold cross-validation. Experimental results show that consensus decision trees are simpler than C4.5 decision trees, indicating that they may be a more stable approximation of the intended domain model than decision trees, constructed from the entire set of training instances.
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Kavšek, B., Lavrač, N., Ferligoj, A. (2002). Hierarchical Clustering of Multiple Decision Trees. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_38
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DOI: https://doi.org/10.1007/978-3-642-56181-8_38
Publisher Name: Springer, Berlin, Heidelberg
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