Naïve, Fast, and Frugal Trees for Classification

Laura F. Martignon, Konstantinos V. Katsikopoulos and Jan K. Woike

in Ecological Rationality

Published in print March 2012 | ISBN: 9780195315448
Published online May 2012 | e-ISBN: 9780199932429 | DOI:
Naïve, Fast, and Frugal Trees for Classification

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Naïve, fast, and frugal trees model simple classification strategies that ignore cue dependencies and process cues sequentially, one at a time. At every level of such a tree a classification is made for one of the considered cue values. This chapter demonstrates that naïve, fast, and frugal trees operate as lexicographic classifiers. On 30 data sets, the performance of such trees is compared with that of two commonly used classification methods: classification and regression trees (CART) and logistic regression. The naïve, fast, and frugal trees are surprisingly robust and their predictive accuracy is comparable to that of savvier competitors, especially when the training set is small. Given that such trees require less time and information and fewer calculations than more computationally complex methods, they represent an attractive option when classifications need to be made quickly and with limited resources.

Keywords: classification; decision tree; lexicographic classifier; splitting profile; classification and regression tree; logistic regression

Chapter.  7355 words.  Illustrated.

Subjects: Cognitive Psychology

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