1. Zoo: Artificial, 7 classes of animals 2. Wine: Using chemical analysis determine the origin of wines 3. Water Treatment Plant: Multiple classes predict plant state 4. University: Data in original (LISP-readable) form 5. Statlog (Vehicle Silhouettes): 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects. 6. 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 7. 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 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. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal. 10. SPECT Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal. 11. Soybean (Large): Michalski's famous soybean disease database 12. Robot Execution Failures: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals 13. Restaurant & consumer data: The dataset was obtained from a recommender system prototype. The task was to generate a top-n list of restaurants according to the consumer preferences. 14. Primary Tumor: From Ljubljana Oncology Institute 15. Planning Relax: The dataset concerns with the classification of two mental stages from recorded EEG signals: Planning (during imagination of motor act) and Relax state. 16. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets 17. Parkinsons: Oxford Parkinson's Disease Detection Dataset 18. Molecular Biology (Promoter Gene Sequences): E. Coli promoter gene sequences (DNA) with partial domain theory 19. 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). 20. Lymphography: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access) 21. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese
name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language). 22. Kinship: Relational dataset 23. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers. 24. Ionosphere: Classification of radar returns from the ionosphere 25. ILPD (Indian Liver Patient Dataset): This data set contains 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos. 26. Housing: Taken from StatLib library 27. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values 28. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney) 29. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach 30. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc) 31. Forest Fires: This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires). 32. Flags: From Collins Gem Guide to Flags, 1986 33. Fertility: 100 volunteers provide a semen sample analyzed according to the WHO 2010 criteria. Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits 34. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack 35. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease. 36. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing 37. Credit Approval: This data concerns credit card applications; good mix of attributes 38. Connectionist Bench (Vowel Recognition - Deterding Data): Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios. 39. Connectionist Bench (Sonar, Mines vs. Rocks): The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock. 40. Congressional Voting Records: 1984 United Stated Congressional Voting Records; Classify as Republican or Democrat 41. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients. 42. Coil 1999 Competition Data: This data set is from the 1999 Computational Intelligence and Learning (COIL) competition. The data contains measurements of river chemical concentrations and algae densities. 43. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast. 44. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database 45. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database 46. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database 47. Automobile: From 1985 Ward's Automotive Yearbook 48. Audiology (Standardized): Standardized version of the original audiology database 49. Annealing: Steel annealing data 50. Abscisic Acid Signaling Network: The objective is to determine the set of boolean rules that describe the interactions of the nodes within this plant signaling network. The dataset includes 300 separate boolean pseudodynamic simulations using an asynchronous update scheme. | 
