Dataset Information
The original Mammography (Woods et al., 1993) data set was made available by the courtesy of Aleksandar Lazarevic. This dataset is publicly available in openML. It has 11,183 samples with 260 calcifications. If we look at predictive accuracy as a measure of goodness of the classifier for this case, the default accuracy would be 97.68% when every sample is labeled non-calcification. But, it is desirable for the classifier to predict most of the calcifications correctly. For outlier detection, the minority class of calcification is considered as outlier class and the non-calcification class as inliers.
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.
Download
File: mammography.mat
Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)
