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  On classification and regression (1998) [26 citations — 5 self]

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by Shinichi Morishita
Discovery Science
http://platinum.ims.u-tokyo.ac.jp/~moris/paper/ds98.ps
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Abstract:

Abstract. We address the problem of computing various types of expressive tests for decision tress and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees. The drawback is that computing an optimal test could be costly. We present a unified framework to approach this problem, and we revisit the design of efficient algorithms for computing important special cases. We also prove that it is intractable to compute an optimal conjunction or disjunction. 1

Citations

3215 C4.5: Programs for machine learning – Quinlan - 1993
2489 Induction of Decision Trees – Quinlan - 1986
2438 Classification and Regression Trees – Breiman, Friedman, et al. - 1984
317 On the hardness of approximating minimization problems – Lund, Yannakakis - 1994
265 Inferring decision trees using the minimum description length principle – Quinlan, Rivest - 1989
104 Data Mining Using TwoDimensional Optimized Association Rules: Scheme, Algorithms and Visualization – Fukuda, Morimoto, et al.
104 Computers and Intractability: A Guide to NP-Completeness – Garey, Johnson - 1979
28 Computing Optimized Rectilinear Regions for Association Rules – Yoda, Fukuda, et al. - 1997
26 Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules – Fukuda, Morimoto, et al. - 1996
15 Polynomial-time solutions to image segmentation – Asano, Chen, et al. - 1996
12 Efficient Construction of Regression Trees with Range and Region Splitting – Morimoto, Ishii, et al. - 2001
3 Private communication – Katoh - 1997