Entropy-based outlier detection using semi-supervised approach with few positive examples

Outlier detection is an important problem in data mining that aims to discover useful exceptional and unusual patterns hidden in large data sets. Fraud detection, time series monitoring, intrusion detection and medical condition monitoring are some of the most common applications of outlier detection. Most existing outlier detection methods are based on supervised or unsupervised learning while some others use semi-supervised approaches. However, in many real world applications, there are not enough labeled data for training and only a few positive labeled samples are available. This paper presents an entropy-based solution. The proposed method consists of two phases. First, reliable negative examples are extracted from positive and unlabeled data and then, as the second phase, the entropy-based outlier detection algorithm is employed to detect top N outliers. Many experiments on real and synthetic data sets are performed. The experimental results on synthetic and real data demonstrated superiority of the proposed outlier detection method in comparison of unsupervised state-of-the-art outlier detection strategies.