  The unlabeled data are generally assumed to be normal data in detecting
abnormal data via semisupervised learning. This assumption, however, causes
inevitable detection error when distribution of unlabeled data is different
from distribution of labeled normal dataset. To deal the problem caused by
distribution gap between labeled and unlabeled data, we propose a
semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The
proposed SAD-KL is composed of two steps: (1) estimating KL divergence of
probability density functions (PDFs) of the local outlier factors (LOFs) of the
labeled normal data and the unlabeled data (2) estimating detection probability
and threshold for detecting normal data in unlabeled data by using the KL
divergence. We show that the PDFs of the LOFs follow Burr distribution and use
them for detection. Once the threshold is computed, the SAD-KL runs iteratively
until the labeling change rate is lower than the predefined threshold.
Experiments results show that the SAD-KL shows superior detection probability
over the existing algorithms even though it takes less learning time.
