1. Abalone: Predict the age of abalone from physical measurements
2. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state.
3. Wilt: High-resolution Remote Sensing data set (Quickbird). Small number of training samples of diseased trees, large number for other land cover. Testing data set from stratified random sample of image.
4. Contraceptive Method Choice: Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.
5. Covertype: Forest CoverType dataset
6. Molecular Biology (Splice-junction Gene Sequences): Primate splice-junction gene sequences (DNA) with associated imperfect domain theory
7. Mushroom: From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible
8. Yeast: Predicting the Cellular Localization Sites of Proteins
9. Dorothea: DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge.
10. p53 Mutants: The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
11. Cardiotocography: The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.
12. Tamilnadu Electricity Board Hourly Readings: This data can be effectively produced the result to fewer parameter of the Load profile can be reduced in the Database
13. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented.
14. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.