1. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
2. LSVT Voice Rehabilitation: 126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem).
3. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks
4. Low Resolution Spectrometer: From IRAS data -- NASA Ames Research Center
5. 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.
6. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
7. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
8. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.