To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an algorithm that (1) is flexible with respect to the outlier definition, (2) wo...
Joel W. Branch, Boleslaw K. Szymanski, Chris Giann...
Abstract. The paper presents a new version of a GMDH type algorithm able to perform an automatic model structure synthesis, robust model parameter estimation and model validation i...
Tatyana I. Aksenova, Vladimir Volkovich, Alessandr...
Abstract. In this paper, we propose a new unsupervised anomaly detection framework for detecting network intrusions online. The framework consists of new anomalousness metrics name...
Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outlie...
Dragoljub Pokrajac, Aleksandar Lazarevic, Longin J...
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this cont...