One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

Detecting instances of unknown categories is an important task for a multitude of problems
such as object recognition, event detection, and defect localization. This article investigates …

Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly
detection approach for seeded bearing faults. Vibration signals from normal bearings and …

One-class support vector machine (OCSVM) is a commonly used one-class classification
method for tackling novelty detection problems. Unfortunately, employing the traditional …

As a commonly used ensemble method, AdaBoost has drawn much consideration in the
field of machine learning. However, AdaBoost is highly sensitive to outliers. The …

Face recognition (FR) plays an important role in video surveillance by allowing to accurately
recognize individuals of interest over a distributed network of cameras. Systems for still-to …

This paper introduces a novel technique for forming efficient one-class classifier ensembles.
It combines an ensemble pruning algorithm with weighted classifier fusion module. The …

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather
than individual feature vectors. In this paper, we address the problem of how these bags can …

Active learning for outlier detection involves users in the process, by asking them for
annotations of observations, in the form of class labels. The usual assumption is that users …

针对传统集成学习方法直接应用于单类分类器效果不理想的问题, 该文首先证明了集成学习方法
能够提升单类分类器的性能, 同时证明了若基分类器集不经选择会导致集成后性能下降; …