Data Set Characteristics: |
Multivariate |
Number of Instances: |
226 |
Area: |
Life |
Attribute Characteristics: |
Categorical |
Number of Attributes: |
N/A |
Date Donated |
1987-12-03 |
Associated Tasks: |
Classification |
Missing Values? |
Yes |
Number of Web Hits: |
78976 |
Source:
Original Owner:
Professor Jergen at Baylor College of Medicine
Donor:
Bruce Porter (porter '@' fall.cs.utexas.EDU)
Data Set Information:
This database does NOT use a standard set of attributes per instance.
Contact Ray Bareiss (rbareiss '@' uunet.uucp ?) for more information.
Domain expert: Professor Craig Wier of the University of Texas, Austin.
Attribute Information:
(all attributes are nominally valued)
1. case identifier.
2. classification (24 classes)
3. List of case features
-- format: form f(v) should be read as "feature f has value v"
Relevant Papers:
Bareiss, E. Ray, & Porter, Bruce (1987). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning, 12-23, Irvine, CA: Morgan Kaufmann.
[Web Link]
Papers That Cite This Data Set1:
 Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004. [View Context].
Marcus Hutter and Marco Zaffalon. Distribution of Mutual Information from Complete and Incomplete Data. CoRR, csLG/0403025. 2004. [View Context].
Richard Nock and Marc Sebban and David Bernard. A SIMPLE LOCALLY ADAPTIVE NEAREST NEIGHBOR RULE WITH APPLICATION TO POLLUTION FORECASTING. International Journal of Pattern Recognition and Artificial Intelligence Vol. 2003. [View Context].
Alexander K. Seewald. How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. ICML. 2002. [View Context].
Alexander K. Seewald and Johann Petrak and Gerhard Widmer. Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. FLAIRS Conference. 2001. [View Context].
Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [View Context].
Jihoon Yang and Rajesh Parekh and Vasant Honavar. DistAl: An inter-pattern distance-based constructive learning algorithm. Intell. Data Anal, 3. 1999. [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].
Pedro Domingos. Unifying Instance-Based and Rule-Based Induction. Machine Learning, 24. 1996. [View Context].
Thomas G. Dietterich and Ghulum Bakiri. Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR, csAI/9501101. 1995. [View Context].
Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995. [View Context].
D. Randall Wilson and Roel Martinez. Improved Center Point Selection for Probabilistic Neural Networks. Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. [View Context].
Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
Geoffrey I Webb. Learning Decision Lists by Prepending Inferred Rules. School of Computing and Mathematics Deakin University. [View Context].
Mohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. [View Context].
Mohammed Waleed Kadous. Expanding the Scope of Concept Learning Using Metafeatures. School of Computer Science and Engineering, University of New South Wales. [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].
Alexander K. Seewald. Meta-Learning for Stacked Classification. Austrian Research Institute for Artificial Intelligence. [View Context].
Bernhard Pfahringer and Ian H. Witten and Philip Chan. Improving Bagging Performance by Increasing Decision Tree Diversity. Austrian Research Institute for AI. [View Context].
Citation Request:
WARNING: This database should be credited to the original owner whenever used for any publication whatsoever.
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