1. Victorian Era Authorship Attribution: To create the largest authorship attribution dataset, we extracted works of 50 well-known authors. To have a non-exhaustive learning, in training there are 45 authors whereas, in the testing, it's 50
2. Relative location of CT slices on axial axis: The dataset consists of 384 features extracted from CT images. The class variable is numeric and denotes the relative location of the CT slice on the axial axis of the human body.
3. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.
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. Health News in Twitter: The data was collected in 2015 using Twitter API. This dataset contains health news from more than 15 major health news agencies such as BBC, CNN, and NYT.
6. Gastrointestinal Lesions in Regular Colonoscopy: This dataset contains features extracted from colonoscopy videos used to detect gastrointestinal lesions. It contains 76 lesions: 15 serrated adenomas, 21 hyperplastic lesions and 40 adenoma.
7. Gas sensor arrays in open sampling settings: The dataset contains 18000 time-series recordings from a chemical detection platform at six different locations in a wind tunnel facility in response to ten high-priority chemical gaseous substances
8. ElectricityLoadDiagrams20112014: This data set contains electricity consumption of 370 points/clients.
9. Dynamic Features of VirusShare Executables: This dataset contains the dynamic features of 107,888 executables, collected by VirusShare from Nov/2010 to Jul/2014.
10. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
11. DeliciousMIL: A Data Set for Multi-Label Multi-Instance Learning with Instance Labels: This dataset includes 1) 12234 documents (8251 training, 3983 test) extracted from DeliciousT140 dataset, 2) class labels for all documents, 3) labels for a subset of sentences of the test documents.
12. APS Failure at Scania Trucks: The datasets' positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS.