The original Vertebral Column dataset from UCI machine learning repository is a multiclass classification dataset having 6 attributes. This biomedical dataset built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre MÃ©dico-Chirurgical de RÃ©adaptation des Massues, Lyon, France. Each patient is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (in this order): pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. The following convention is used for the class labels: Normal (NO) and Abnormal (AB). Here, “AB” is the majority class having 210 instances which are used as inliers and “NO” is downsampled from 100 to 30 instances as outliers.
Saket Sathe and Charu C. Aggarwal. LODES: Local Density meets Spectral Outlier Detection. SIAM Conference on Data Mining, 2016.
Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)