1. 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.
2. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
3. 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.
4. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.
5. 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