Shuttle dataset

Dataset information

The original Statlog (Shuttle) dataset from UCI machine learning repository is a multi-class classification dataset with dimensionality 9. Here, the training and test data are combined. The smallest five classes, i.e. 2, 3, 5, 6, 7 are combined to form the outliers class, while class 1 forms the inlier class. Data for class 4 is discarded.

Source (citation)

Abe, Naoki, Bianca Zadrozny, and John Langford. “Outlier detection by active learning.Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.

Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation forest.2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008.

K. M. Ting, J. T. S. Chuan, and F. T. Liu. “Mass: A New Ranking Measure for Anomaly Detection.“, IEEE Transactions on Knowledge and Data Engineering, 2009.

Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu & Tan Swee Chuan. (2010). Mass Estimation and Its Applications. Proceedings of The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2010. pp. 989-998.

Swee Chuan Tan, Kai Ming Ting & Fei Tony Liu. (2011). Fast Anomaly Detection for Streaming Data. Proceedings of the International Joint Conference on Artificial Intelligence 2011. pp.1151-1156.


File: shuttle.mat

Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)