Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings

© 2016 IEEE. Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data. CUFS quantifies the outlierness (or relevance) of features by learning and integrating both the feature value couplings and feature couplings. Such value-To-feature couplings capture intrinsic data characteristics and distinguish relevant features from those noisy/redundant features. CUFS is further instantiated into a parameter-free Dense Subgraph-based Feature Selection method, called DSFS. We prove that DSFS retains a 2-Approximation feature subset to the optimal subset. Extensive evaluation results on 15 real-world data sets show that DSFS obtains an average 48% feature reduction rate, and enables three different types of pattern-based outlier detection methods to achieve substantially better AUC improvements and/or perform orders of magnitude faster than on the original feature set. Compared to its feature selection contender, on average, all three DSFS-based detectors achieve more than 20% AUC improvement

CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters

Homophily Outlier Detection in Non-IID Categorical Data

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10%-28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.Comment: To appear in Data Ming and Knowledge Discovery Journa

A Survey on Explainable Anomaly Detection

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version

Outlier Detection Ensemble with Embedded Feature Selection

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.Comment: 10pages, AAAI202

A survey on explainable anomaly detection

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