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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)
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