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A statistical approach to decision tree modeling

Author:

Michael I. JordanAuthors Info & Claims

COLT '94: Proceedings of the seventh annual conference on Computational learning theory

Pages 13 - 20

https://doi.org/10.1145/180139.175372

Published: 16 July 1994 Publication History

Abstract

A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields a likelihood measure of goodness of fit, allowing ML and MAP estimation techniques to be utilized. An efficient algorithm is presented to estimate the parameters in the tree. The model selection problem is presented and several alternative proposals are considered. A hidden Markov version of the tree is described for data sequences that have temporal dependencies.

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Jordan, M. I. & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.

Digital Library

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Jordan, M. I., & Xu, L. (1993). Convergence properties of the EM approach to learning in mixture-of-experts architectures. MIT Artificial Intelligence Laboratory Tech. Rep. 1458, Cambridge, MA.

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Meila, M. P., & Jordan, M. I. (1994). Learning the parameters of HMMs with auxiliary input. MIT Computational Cognitive Science Tech. Rep. 9401, Cambridge, MA.

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Neal, R., & Hinton, G. E. (1993). A new view of the EM algorithm that justifies incremental and other variants. Submitted to Biometrika.

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Quinlan, J. R., & Rivest, R. L. (1989). Inferring decision trees using the Minimum Description Length Principle. Information and Computation, 80, 227-248.

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Utgoff, P. E., & Brodley, C. E. (1990). An incremental method for finding multivariate splits for decision trees. In Proceedings of the Seventh International Conference on Machine Learning, Los Altos, CA.

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Index Terms

A statistical approach to decision tree modeling
1. Computing methodologies
  1. Machine learning
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Conferences

COLT '94: Proceedings of the seventh annual conference on Computational learning theory

July 1994

376 pages

ISBN:0897916557

DOI:10.1145/180139

Chairman:
Manfred Warmuth
Univ. of California, Santa Cruz

Copyright © 1994 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 July 1994

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7COLT94

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7COLT94: 7th Annual Conference on Computational Learning Theory

July 12 - 15, 1994

New Jersey, New Brunswick, USA

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Overall Acceptance Rate 35 of 71 submissions, 49%

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Cited By

Muduli DBhatta SShookdeb SAdhikari ASapkota AChaturvedi P(2024)Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree Ensemble Approach2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725813(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10725813
Fu LWang LWang HYang MYang QLin YGuan SDeng YLiu LLi QHe MZhang PChen HDeng G(2023)A cross-sectional study: a breathomics based pulmonary tuberculosis detection methodBMC Infectious Diseases10.1186/s12879-023-08112-323:1Online publication date: 10-Mar-2023
https://doi.org/10.1186/s12879-023-08112-3
Zhang PRen TChen HLi QHe MFeng YWang LHuang TYuan JDeng GLu H(2022)A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometryJournal of Breath Research10.1088/1752-7163/ac8ea116:4(046009)Online publication date: 12-Sep-2022
https://doi.org/10.1088/1752-7163/ac8ea1
Armandpour MSadeghian AZhou MRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Convex polytope treesProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540646(5038-5051)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3540646
Hehn TKooij JHamprecht F(2019)End-to-End Learning of Decision Trees and ForestsInternational Journal of Computer Vision10.1007/s11263-019-01237-6Online publication date: 9-Oct-2019
https://doi.org/10.1007/s11263-019-01237-6
Hehn THamprecht F(2019)End-to-End Learning of Deterministic Decision TreesPattern Recognition10.1007/978-3-030-12939-2_42(612-627)Online publication date: 14-Feb-2019
https://doi.org/10.1007/978-3-030-12939-2_42
Kautz TGroh BHannink JJensen UStrubberg HEskofier B(2017)Activity recognition in beach volleyball using a Deep Convolutional Neural NetworkData Mining and Knowledge Discovery10.1007/s10618-017-0495-031:6(1678-1705)Online publication date: 1-Nov-2017
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Carcenac MRedif S(2016)A highly scalable modular bottleneck neural network for image dimensionality reduction and image transformationApplied Intelligence10.1007/s10489-015-0715-544:3(557-610)Online publication date: 1-Apr-2016
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Li TYu SYang F(2013)The Method of Tree Dynamic Modeling under Rainfall FieldApplied Mechanics and Materials10.4028/www.scientific.net/AMM.475-476.1551475-476(1551-1554)Online publication date: Dec-2013
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