MONK's Problems
Donated on 9/30/1992
A set of three artificial domains over the same attribute space; Used to test a wide range of induction algorithms
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical
# Instances
432
# Features
6
Dataset Information
Additional 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.
Has Missing Values?
No
Introductory Paper
By S. Thrun. 1991
Published in Carnegie Mellon University
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| class | Target | Binary | no | ||
| a1 | Feature | Integer | no | ||
| a2 | Feature | Integer | no | ||
| a3 | Feature | Integer | no | ||
| a4 | Feature | Integer | no | ||
| a5 | Feature | Integer | no | ||
| a6 | Feature | Integer | no | ||
| ID | ID | Categorical | no |
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Additional Variable 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)
Baseline Model Performance
Papers Citing this Dataset
By Hassen Dhrif, Luis Giraldo, Miroslav Kubat, Stefan Wuchty. 2019
Published in ArXiv.
By Chengxiang Yin, Hongjun Zhang, Rui Zhang, Zilin Zeng, Xiuli Qi, Yuntian Feng. 2018
Published in IEICE Transactions.
By Brigitte Chebel-Morello, Simon Malinowski, Hafida Senoussi. 2015
Published in Applied Intelligence.
By Mohammadreza Doostmohammadian, Usman Khan. 2014
Published in ArXiv.
By Ying Yang, Xindong Wu, Xingquan Zhu. 2008
Published in Data Knowl. Eng..
0 to 5 of 9
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset monk_s_problems = fetch_ucirepo(id=70) # data (as pandas dataframes) X = monk_s_problems.data.features y = monk_s_problems.data.targets # metadata print(monk_s_problems.metadata) # variable information print(monk_s_problems.variables)
Wnek,J.. (1992). MONK's Problems. UCI Machine Learning Repository. https://doi.org/10.24432/C5R30R.
@misc{misc_monk's_problems_70,
author = {Wnek,J.},
title = {{MONK's Problems}},
year = {1992},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5R30R}
}Creators
J. Wnek
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.