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A set of three artificial domains over the same attribute space; Used to test a wide range of induction algorithms......

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    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Donor:

    Sebastian Thrun
    School of Computer Science
    Carnegie Mellon University
    Pittsburgh, PA 15213, USA
    E-mail: thrun '@' cs.cmu.edu


    Data Set Information:

    The MONK's problem were the basis of a first international comparison of learning algorithms. The result of this comparison is summarized in "The MONK's Problems - A Performance Comparison of Different Learning algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S. Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec. 1991.

    One significant characteristic of this comparison is that it was performed by a collection of researchers, each of whom was an advocate of the technique they tested (often they were the creators of the various methods). In this sense, the results are less biased than in comparisons performed by a single person advocating a specific learning method, and more accurately reflect the generalization behavior of the learning techniques as applied by knowledgeable users.

    There are three MONK's problems. The domains for all MONK's problems are the same (described below). One of the MONK's problems has noise added. For each problem, the domain has been partitioned into a train and test set.


    Attribute Information:

    1. class: 0, 1
    2. a1: 1, 2, 3
    3. a2: 1, 2, 3
    4. a3: 1, 2
    5. a4: 1, 2, 3
    6. a5: 1, 2, 3, 4
    7. a6: 1, 2
    8. Id: (A unique symbol for each instance)


    Relevant Papers:

    Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation, School of Information Technology and Engineering, Reports of Machine Learning and Inference Laboratory, MLI 93-2, Center for Artificial Intelligence, George Mason University, March 1993.
    [Web Link]

    Wnek, J. and Michalski, R.S., "Comparing Symbolic and Subsymbolic Learning: Three Studies," in Machine Learning: A Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.), Morgan Kaufmann, San Mateo, CA, 1993.
    [Web Link]

    See File: thrun.comparison.ps.Z


    Papers That Cite This Data Set1:

    Jianbin Tan and David L. Dowe. MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes. Australian Conference on Artificial Intelligence. 2003. [

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