1. Thoracic Surgery Data: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.
2. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
3. seeds: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.
4. Pima Indians Diabetes: From National Institute of Diabetes and Digestive and Kidney Diseases; Includes cost data (donated by Peter Turney)
5. Parkinsons: Oxford Parkinson's Disease Detection Dataset
6. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
7. Mammographic Mass: Discrimination of benign and malignant mammographic masses based on BI-RADS attributes and the patient's age.
8. Iris: Famous database; from Fisher, 1936
9. 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.
10. Haberman's Survival: Dataset contains cases from study conducted on the survival of patients who had undergone surgery for breast cancer
11. 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
12. Ecoli: This data contains protein localization sites
13. Demospongiae: Marine sponges of the Demospongiae class classification domain.
14. Daphnet Freezing of Gait: This dataset contains the annotated readings of 3 acceleration sensors at the hip and leg of Parkinson's disease patients that experience freezing of gait (FoG) during walking tasks.
15. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.
16. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database
17. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database
18. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database
19. Arcene: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge.
20. 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.