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Chess (King-Rook vs. King-Knight) Data Set
Download: Data Folder, Data Set Description

Abstract: Knight Pin Chess End-Game Database Creator

Data Set Characteristics:  

Multivariate, Data-Generator

Number of Instances:

N/A

Area:

Game

Attribute Characteristics:

Categorical, Integer

Number of Attributes:

22

Date Donated

1988-10-03

Associated Tasks:

Classification

Missing Values?

No

Number of Web Hits:

23012


Source:

Database originally described by Ross Quinlan.

Donor/Coder:

Jeff Schlimmer (Jeff.Schlimmer '@' cs.cmu.edu)


Data Set Information:

The companion file is a Common Lisp demonstration file that generates knight-pin Chess end-game samples. Start up Lisp and load the file. It generates 100 end-games and writes them to a separate file. Look at the end of the file to see how to change it so that it will produce more end-games, or use the file for output that you wish.

The code is released for experimental, confidential use only. See the end of the file for load-time commands that generate a file of examples in Quinlan's format.

Note: this program generates duplicates. In one run, there were about 370 duplicates in the first 1000 instances (i.e., 630 distinct examples).


Attribute Information:

Attribute Summaries:
Class: knight's side is lost in n-ply (n=2, 3, etc)
1. distance from black king to knight: 1, 2, >2
2. distance from black king to rook: 1, 2, >2
3. distance from black king to white king: 1, 2, >2
4. distance from white king to knight: 1, 2, >2
5. distance from white king to rook: 1, 2, >2
6. distance from rook to knight (ADDED): 1, 2, >2
7. board relationship of black king and knight (ADDED): diag, rect, other
8. board relationship of black king and rook (ADDED): diag, rect, other
9. board relationship of black king and white king (ADDED): diag,rect,other
10. board relationship of white king and knight (ADDED): diag, rect, other
11. board relationship of white king and rook (ADDED): diag, rect, other
12. board relationship of white rook and knight (ADDED): diag, rect, other
13. type of black king's initial square: corner, edge, open
14. type of black knight's initial square (ADDED): corner, edge, open
15. type of white king's initial square (ADDED): corner, edge, open
16. type of white rook's initial square (ADDED): corner, edge, open
17. rook checks black king (OMITTED, always f): t, f
18. rook threatens knight (OMITTED, always t): t, f
19. knight threatens rook (OMITTED, always f): t, f
20. black king, knight, rook in line (OMITTED, always t) t, f
21. black king can move adjacent to knight (OMITTED) t, f
22. knight can interpose adjacent to king (OMITTED) t, f


Relevant Papers:

Quinlan, J.R. (1983). Learning Efficient Classification Procedures and Their Application to Chess End Games. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine Learning -- An Artificial Intelligence Approach, 463-482, Palo Alto: Tioga.
[Web Link]


Papers That Cite This Data Set1:

Manuel Oliveira. Library Release Form Name of Author: Stanley Robson de Medeiros Oliveira Title of Thesis: Data Transformation For Privacy-Preserving Data Mining Degree: Doctor of Philosophy Year this Degree Granted. University of Alberta Library. 2005. [View Context].

Marcus Hutter and Marco Zaffalon. Distribution of Mutual Information from Complete and Incomplete Data. CoRR, csLG/0403025. 2004. [View Context].

Ira Cohen and Fabio Gagliardi Cozman and Nicu Sebe and Marcelo Cesar Cirelo and Thomas S. Huang. Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction. IEEE Trans. Pattern Anal. Mach. Intell, 26. 2004. [View Context].

Douglas Burdick and Manuel Calimlim and Jason Flannick and Johannes Gehrke and Tomi Yiu. MAFIA: A Performance Study of Mining Maximal Frequent Itemsets. FIMI. 2003. [View Context].

James Bailey and Thomas Manoukian and Kotagiri Ramamohanarao. Fast Algorithms for Mining Emerging Patterns. PKDD. 2002. [View Context].

Russell Greiner and Wei Zhou. Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. AAAI/IAAI. 2002. [View Context].

Tanzeem Choudhury and James M. Rehg and Vladimir Pavlovic and Alex Pentland. Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection. ICPR (3). 2002. [View Context].

Marco Zaffalon and Marcus Hutter. Robust Feature Selection by Mutual Information Distributions. CoRR, csAI/0206006. 2002. [View Context].

Michael G. Madden. Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002. [View Context].

Jie Cheng and Russell Greiner. Learning Bayesian Belief Network Classifiers: Algorithms and System. Canadian Conference on AI. 2001. [View Context].

Boonserm Kijsirikul and Sukree Sinthupinyo and Kongsak Chongkasemwongse. Approximate Match of Rules Using Backpropagation Neural Networks. Machine Learning, 44. 2001. [View Context].

Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao and Limsoon Wong. DeEPs: A New Instance-based Discovery and Classification System. Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases. 2001. [View Context].

Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao. Instance-Based Classification by Emerging Patterns. PKDD. 2000. [View Context].

Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999. [View Context].

Adam J. Grove and Dale Schuurmans. Boosting in the Limit: Maximizing the Margin of Learned Ensembles. AAAI/IAAI. 1998. [View Context].

Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998. [View Context].

Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996. [View Context].

Brian R. Gaines. Structured and Unstructured Induction with EDAGs. KDD. 1995. [View Context].

Ron Kohavi and Dan Sommerfield. Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology. KDD. 1995. [View Context].

M. A. Galway and Michael G. Madden. DEPARTMENT OF INFORMATION TECHNOLOGY technical report NUIG-IT-011002 Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. Department of Information Technology National University of Ireland, Galway. [View Context].

BayesianClassifi552 Pat Langley and Wayne Iba. In Proceedings of the Tenth National ConferenceonArtifi256 Intelligence( 42840. Lambda Kevin Thompson. [View Context].

Jerome H. Friedman and Ron Kohavi and Youngkeol Yun. To appear in AAAI-96 Lazy Decision Trees. Statistics Department and Stanford Linear Accelerator Center Stanford University. [View Context].

Grigorios Tsoumakas and Ioannis P. Vlahavas. Fuzzy Meta-Learning: Preliminary Results. Greek Secretariat for Research and Technology. [View Context].

Nikunj C. Oza and Stuart J. Russell. Online Bagging and Boosting. Computer Science Division University of California. [View Context].

Hankil Yoon and Khaled A. Alsabti and Sanjay Ranka. Tree-based Incremental Classification for Large Datasets. CISE Department, University of Florida. [View Context].

Omid Madani and David M. Pennock and Gary William Flake. Co-Validation: Using Model Disagreement to Validate Classification Algorithms. Yahoo! Research Labs. [View Context].


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[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

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