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Finding Temporal Relations: Causal Bayesian Networks vs. C4.5 (2000)  (Make Corrections)  (2 citations)
Kamran Karimi and Howard J. Hamilton Department of Computer Science...
International Syposium on Methodologies for Intelligent Systems



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Abstract: Observing the world and finding trends and relations among the variables of interest is an important and common learning activity. In this paper we apply TETRAD, a program that uses Bayesian networks to discover causal rules, and C4.5, which creates decision trees, to the problem of discovering relations among a set of variables in the controlled environment of an Artificial Life simulator. All data in this environment are generated by a single entity over time. The rules in the domain... (Update)

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...that depends on the others. Though C4.5 has been traditionally used as a classifier, it can also be used to find temporal relations [3]. C4.5 uses a greedy algorithm with one look ahead step. It computes the information contents of each condition attribute, and the results...

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Knowledge Discovery from Sequential Data - Höppner (2003)   (Correct)
Logical Decision Rules: Teaching C4.5 to Speak Prolog - Karimi, Hamilton   (Correct)

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2:   Finding informative rules in interval sequences - oppner, Klawonn - 2001
2:   Programs for machine learning (context) - Quinlan - 1993

BibTeX entry:   (Update)

Karimi, K. and Hamilton, H. J., "Finding Temporal Relations: Causal Bayesian Networks vs. C4.5." The 12th International Symposium on Methodologies for Intelligent Systems (ISMIS'2000), Charlotte, NC, USA. http://citeseer.ist.psu.edu/karimi00finding.html   More

@inproceedings{ karimi00finding,
    author = "Kamran Karimi and Howard J. Hamilton",
    title = "Finding Temporal Relations: Causal Bayesian Networks vs. C4.5",
    booktitle = "International Syposium on Methodologies for Intelligent Systems",
    pages = "266-273",
    year = "2000",
    url = "citeseer.ist.psu.edu/karimi00finding.html" }
Citations (may not include all citations):
2177   Programs for Machine Learning (context) - Quinlan - 1993
663   Some Philosophical Problems from the Standpoint of Artificia.. (context) - McCarthy, Hayes - 1969
37   Scalable Techniques for Mining Causal Structures - Silverstein, Brin et al. - 1998
22   A Bayesian Approach to Learning Causal Networks - Heckerman - 1995
20   Artificial Life: A Quest for a New Creation (context) - Levy - 1992
8   Tetrad II: Tools for Causal Modeling (context) - Scheines, Spirtes et al. - 1994
3   Are There Algorithms that Discover Causal Structure (context) - Freedman, Humphreys - 1998
2   Reply to Freedman (context) - Spirtes, Scheines - 1997
1   and Wallace (context) - Korb - 1997
1   A Comparison of Association Rule Discovery and Bayesian Netw.. - Bowes, Neufeld et al.
1   British Journal of the Philosophy of Science (context) - Humphreys, Freedman - 1996
ftp://orion.cs.uregina.ca/pub/ural/URAL.java

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