1. 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. 2. Annealing: Steel annealing data 3. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing 4. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc) 5. Ionosphere: Classification of radar returns from the ionosphere 6. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks 7. Low Resolution Spectrometer: From IRAS data -- NASA Ames Research Center 8. Wine: Using chemical analysis determine the origin of wines 9. 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 10. 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. 11. Water Treatment Plant: Multiple classes predict plant state 12. Function Finding: Cases collected mostly from investigations in physical science; intention is to evaluate function-finding algorithms 13. 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). 14. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity. | 
