77 lines
2.8 KiB
Plaintext
77 lines
2.8 KiB
Plaintext
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1. Title: The Monk's Problems
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2. Sources:
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(a) Donor: Sebastian Thrun
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School of Computer Science
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Carnegie Mellon University
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Pittsburgh, PA 15213, USA
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E-mail: thrun@cs.cmu.edu
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(b) Date: October 1992
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3. Past Usage:
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- See File: thrun.comparison.ps.Z
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- Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation,
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School of Information Technology and Engineering, Reports of Machine
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Learning and Inference Laboratory, MLI 93-2, Center for Artificial
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Intelligence, George Mason University, March 1993.
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- Wnek, J. and Michalski, R.S., "Comparing Symbolic and
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Subsymbolic Learning: Three Studies," in Machine Learning: A
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Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.),
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Morgan Kaufmann, San Mateo, CA, 1993.
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4. Relevant Information:
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The MONK's problem were the basis of a first international comparison
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of learning algorithms. The result of this comparison is summarized in
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"The MONK's Problems - A Performance Comparison of Different Learning
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algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B.
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Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher,
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R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S.
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Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de
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Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
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Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
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1991.
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One significant characteristic of this comparison is that it was
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performed by a collection of researchers, each of whom was an advocate
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of the technique they tested (often they were the creators of the
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various methods). In this sense, the results are less biased than in
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comparisons performed by a single person advocating a specific
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learning method, and more accurately reflect the generalization
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behavior of the learning techniques as applied by knowledgeable users.
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There are three MONK's problems. The domains for all MONK's problems
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are the same (described below). One of the MONK's problems has noise
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added. For each problem, the domain has been partitioned into a train
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and test set.
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5. Number of Instances: 432
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6. Number of Attributes: 8 (including class attribute)
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7. Attribute information:
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1. class: 0, 1
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2. a1: 1, 2, 3
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3. a2: 1, 2, 3
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4. a3: 1, 2
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5. a4: 1, 2, 3
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6. a5: 1, 2, 3, 4
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7. a6: 1, 2
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8. Id: (A unique symbol for each instance)
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8. Missing Attribute Values: None
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9. Target Concepts associated to the MONK's problem:
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MONK-1: (a1 = a2) or (a5 = 1)
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MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
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MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
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(5% class noise added to the training set)
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