A parameter based growing ensemble of self-organizing maps for outlier detection in healthcare

Outlier detection is critical for many applications such as healthcare, health insurance, medical diagnosis, predictive analytics, pattern recognition, intrusion detection, anomaly or defect detection, video surveillance, credit card fraud detection and text mining. Outlier detection techniques could be statistics, distance- or model based. Techniques, which are based on a single method for outlier detection usually have weaknesses and strengths and are mostly unstable. Outlier detection ensembles harness the strengths of individual detectors and result in stable performance. This paper presents a new parameter based growing self-organizing maps ensemble (GSOME) for outlier detection in multivariate patterns. For outlier detection, the proposed GSOME transforms non-linear relationships between high dimensional patterns into a simple 1D geometric relationship. Whatever the pattern dimensionality is, it is mapped to a single point of a line. The dispersion of mapped points will be used to locate the outliers and measure the degree of outlyingness. Several experiments on both real and synthetic data sets show the promising performance of the proposed GSOME.