1. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
2. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
3. Acute Inflammations: The data was created by a medical expert as a data set to test the expert system,
which will perform the presumptive diagnosis of two diseases of the urinary system.
4. Mechanical Analysis: Fault diagnosis problem of electromechanical devices; also PUMPS DATA SET is newer version with domain theory and results
5. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack
6. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
7. Statlog (Heart): This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form
8. Statlog (Australian Credit Approval): This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form
9. Credit Approval: This data concerns credit card applications; good mix of attributes
10. University: Data in original (LISP-readable) form
11. Zoo: Artificial, 7 classes of animals
12. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)
13. Statlog (German Credit Data): This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix
14. Meta-data: Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project).
15. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values
16. Flags: From Collins Gem Guide to Flags, 1986
17. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.
18. Annealing: Steel annealing data
19. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing
20. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
21. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.