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. Urban Land Cover: Classification of urban land cover using high resolution aerial imagery. Intended to assist sustainable urban planning efforts.
4. NoisyOffice: Corpus intended to do cleaning (or binarization) and enhancement of noisy grayscale printed text images using supervised learning methods. Noisy images and their corresponding ground truth provided.
5. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks
6. Low Resolution Spectrometer: From IRAS data -- NASA Ames Research Center
7. 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.
8. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
9. Gas sensor array exposed to turbulent gas mixtures: A chemical detection platform composed of 8 chemoresistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided.
10. 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.
11. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.