github.com/vlifesystems/rulehunter@v0.0.0-20180501090014-673078aa4a83/examples/csv/monks.txt (about)

     1  1. Title: The Monk's Problems
     2  
     3  2. Sources:
     4      (a) Donor: Sebastian Thrun
     5  	       School of Computer Science
     6  	       Carnegie Mellon University
     7  	       Pittsburgh, PA 15213, USA
     8  
     9  	       E-mail: thrun@cs.cmu.edu
    10           Web: http://robots.stanford.edu/papers/thrun.MONK.html
    11  
    12      (b) Date: October 1992
    13  
    14  Data Source:
    15    UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
    16    Irvine, CA: University of California, School of Information and
    17    Computer Science.
    18  	http://archive.ics.uci.edu/ml/datasets/MONK%27s+Problems
    19  
    20  
    21  3. Past Usage:
    22  
    23     - See File: thrun.comparison.ps.Z
    24  
    25     - Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation,
    26       School of Information Technology and Engineering, Reports of Machine
    27       Learning and Inference Laboratory, MLI 93-2, Center for Artificial
    28       Intelligence, George Mason University, March 1993.
    29  
    30     - Wnek, J. and Michalski, R.S., "Comparing Symbolic and
    31       Subsymbolic Learning: Three Studies," in Machine Learning: A
    32       Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.),
    33       Morgan Kaufmann, San Mateo, CA, 1993.
    34  
    35  4. Relevant Information:
    36  
    37     The MONK's problem were the basis of a first international comparison
    38     of learning algorithms. The result of this comparison is summarized in
    39     "The MONK's Problems - A Performance Comparison of Different Learning
    40     algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I.  Bratko, B.
    41     Cestnik, J. Cheng, K. De Jong, S.  Dzeroski, S.E. Fahlman, D. Fisher,
    42     R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J.  Kreuziger, R.S.
    43     Michalski, T. Mitchell, P.  Pachowicz, Y. Reich H.  Vafaie, W. Van de
    44     Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
    45     Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
    46     1991.
    47  
    48     One significant characteristic of this comparison is that it was
    49     performed by a collection of researchers, each of whom was an advocate
    50     of the technique they tested (often they were the creators of the
    51     various methods). In this sense, the results are less biased than in
    52     comparisons performed by a single person advocating a specific
    53     learning method, and more accurately reflect the generalization
    54     behavior of the learning techniques as applied by knowledgeable users.
    55  
    56     There are three MONK's problems.  The domains for all MONK's problems
    57     are the same (described below).  One of the MONK's problems has noise
    58     added. For each problem, the domain has been partitioned into a train
    59     and test set.
    60  
    61  5. Number of Instances: 432
    62  
    63  6. Number of Attributes: 8 (including class attribute)
    64  
    65  7. Attribute information:
    66      1. class: 0, 1
    67      2. a1:    1, 2, 3
    68      3. a2:    1, 2, 3
    69      4. a3:    1, 2
    70      5. a4:    1, 2, 3
    71      6. a5:    1, 2, 3, 4
    72      7. a6:    1, 2
    73      8. Id:    (A unique symbol for each instance)
    74  
    75  8. Missing Attribute Values: None
    76  
    77  9. Target Concepts associated to the MONK's problem:
    78  
    79     MONK-1: (a1 = a2) or (a5 = 1)
    80  
    81     MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
    82  
    83     MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
    84             (5% class noise added to the training set)