Introduction

Outlier detection is an important subject in machine learning and data analysis. The term outlier refers to abnormal observations that are inconsistent with the bulk of the data distribution [16, 32, 98, 240, 265, 266, 287]. Some sample applications are as follows.

1. • Detection of imposters or rejection of unauthorized access to computer networks.

2. • Genomic research – identifying abnormal gene or protein sequences.

3. • Biomedical, e.g. ECG arrythmia monitoring.

4. • Environmental safety detection, where outliers indicate abnormality.

5. • Personal safety, with security aids embedded in mobile devices.

For some real-world application examples, see e.g Hodge and Austin [98].

The standard approach to outlier detection is density-based, whereby the detection depends on the outlier's relationship with the bulk of the data. Many algorithms use concepts of proximity and/or density estimation in order to find outliers. However, in high-dimensional spaces the data become increasingly sparse and the notion of proximity/density has become less meaningful, and consequently model-based methods have become more appealing [1, 39]. It is also a typical assumption that a model is to be trained from only one type of (say, positive) training patterns, making it a fundamentally different problem, and thereby creating a new learning paradigm leading to one-class-based learning models.

SVM-type learning models are naturally amenable to outlier detection since certain support vectors can be identified as outliers.