Abstract
There are a number of classification methods for applications involving categorical data [either nominal (unordered categories) or ordinal (ordered categories)], where the objects are described by lists of attributes. A popular method is the decision tree, which uses a branching structure with a series of questions. The questions should be organized so that the most informative are asked first. The information gain is a descriptor of the relative utility of different questions (addressing particular features) at each node of the tree, and it can be formulated in terms of entropy.
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References
Cohen, W.: Fast effective rule induction. In: Prieditis, A., Russell, S.J. (eds.) Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Mateo, CA (1995)
Furnkranz, J., Widmer, G.: Incremental reduced error pruning. In: Cohen, W., Hirsch, H. (eds.) Eleventh International Conference on Machine Learning, pp. 70–77. Morgan Kaufmann, San Mateo, CA (1994)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA (1993)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Mateo, CA (2005)
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Dougherty, G. (2013). Nonmetric Methods. In: Pattern Recognition and Classification. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5323-9_3
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DOI: https://doi.org/10.1007/978-1-4614-5323-9_3
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