Expressive Tests for Classification and Regression
Summary :
We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. 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.
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- IEICE TRANSACTIONS on Information Vol.E83-D No.1 pp.52-60
- Publication Date
- 2000/01/25
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- Special Section INVITED PAPER (Special Issue on Surveys on Discovery Science)
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Shinichi MORISHITA, Akihiro NAKAYA, "Expressive Tests for Classification and Regression" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 1, pp. 52-60, January 2000, doi: .
Abstract: We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_1_52/_p
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@ARTICLE{e83-d_1_52,
author={Shinichi MORISHITA, Akihiro NAKAYA, },
journal={IEICE TRANSACTIONS on Information},
title={Expressive Tests for Classification and Regression},
year={2000},
volume={E83-D},
number={1},
pages={52-60},
abstract={We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. 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.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Expressive Tests for Classification and Regression
T2 - IEICE TRANSACTIONS on Information
SP - 52
EP - 60
AU - Shinichi MORISHITA
AU - Akihiro NAKAYA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2000
AB - We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. 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.
ER -
English
