Datasets:
| 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) | |