Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class Classification

Density estimation methods are often regarded as unsuitable for anomaly detection in high-dimensional data due to the difficulty of estimating multivariate probability distributions. Instead, the scores from popular distanceand localdensity-based methods, such as local outlier factor (LOF), are used as surrogates for probability densities. We question this infeasibility assumption and explore a family of simple statistically-based density estimates constructed by combining a probabilistic classifier with a naive density estimate. Across a number of semi-supervised and unsupervised problems formed from real-world data sets, we show that these methods are competitive with LOF and that even simple density estimates that assume attribute independence can perform strongly. We show that these density estimation methods scale well to data with high dimensionality and that they are robust to the problem of irrelevant attributes that plagues methods based on local estimates.