# Info on Monks Data Bases

    1. Title: The Monk's Problems

    2. Sources:
        (a) Donor: Sebastian Thrun
    	       School of Computer Science
    	       Carnegie Mellon University
    	       Pittsburgh, PA 15213, USA

    	       E-mail: thrun@cs.cmu.edu

        (b) Date: October 1992

    3. Past Usage:

       - See File: thrun.comparison.ps.Z

       - 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.

       - 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.

    4. Relevant 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.

    5. Number of Instances: 432

    6. Number of Attributes: 8 (including class attribute)

    7. 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)

    8. Missing Attribute Values: None

    9. Target Concepts associated to the MONK's problem:

       MONK-1: (a1 = a2) or (a5 = 1)

       MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}

       MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
               (5% class noise added to the training set)