The original ForestCover/Covertype dataset from UCI machine learning repository is a multiclass classification dataset. It is used in predicting forest cover type from cartographic variables only (no remotely sensed data). This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices. This dataset has 54 attributes (10 quantitative variables, 4 binary wilderness areas and 40 binary soil type variables). Here, outlier detection dataset is created using only 10 quantitative attributes. Instances from class 2 are considered as normal points and instances from class 4 are anomalies. The anomalies ratio is 0.9%. Instances from the other classes are omitted.
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.
Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)