Feature bagging for outlier detection

TLDR

Outlier detection has recently become an important problem in many industrial and financial applications. The paper proposes a novel feature‑bagging method to detect outliers in very large, high‑dimensional, noisy databases. It combines multiple outlier detectors, each trained on a random subset of features, and aggregates their outlier scores to improve detection quality. Experiments on synthetic and real datasets demonstrate that combining outputs from multiple detectors yields non‑trivial improvements over individual algorithms.

Abstract

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in very large, high dimensional and noisy databases is proposed. It combines results from multiple outlier detection algorithms that are applied using different set of features. Every outlier detection algorithm uses a small subset of features that are randomly selected from the original feature set. As a result, each outlier detector identifies different outliers, and thus assigns to all data records outlier scores that correspond to their probability of being outliers. The outlier scores computed by the individual outlier detection algorithms are then combined in order to find the better quality outliers. Experiments performed on several synthetic and real life data sets show that the proposed methods for combining outputs from multiple outlier detection algorithms provide non-trivial improvements over the base algorithm.