Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

Novelty detection is especially important for monitoring safety-critical systems in which novel
conditions rarely occur and knowledge about novelty in that system is often limited or …

A new chiller fault detection and diagnosis (FDD) method is proposed in this article. Different
from conventional chiller FDD methods, this article considers the FDD problem as a typical …

Active learning methods increase classification quality by means of user feedback. An
important subcategory is active learning for outlier detection with one-class classifiers. While …

Detection of outliers and boundary points represents an effective, interesting and potentially
valuable pattern, which may be more important than that of normal points. In order to detect …

It is time consuming to train support vector regression (SVR) for large-scale problems even
with efficient quadratic programming solvers. This issue is particularly serious when tuning …

Detecting boundary points (including outliers) is often more interesting than detecting
normal observations, since they represent valid, interesting, and potentially valuable …

There are a large number of target data and fewer outlier data in the problem of one-class
classification. The sample reduction methods used for two-class or multi-class classification …

Instance reduction techniques are data preprocessing methods originally developed to
enhance the nearest neighbor rule for standard classification. They reduce the training data …