We propose to use learning vector quantization (LVQ) in novelty detection where a few outliers exist in training data. The codebook update of original LVQ is modified and the sche...
Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this...
Adriano L. I. Oliveira, Fernando Buarque de Lima N...
Over the last 25+ years, the software community has been searching for the best models for estimating variables of interest (e.g., cost, defects, and fault proneness). However, li...
We consider the problem of detecting anomalies in high arity categorical datasets. In most applications, anomalies are defined as data points that are 'abnormal'. Quite ...
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...