12 research outputs found
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
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
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
Credit card fraud detection is a very challenging problem because of the
specific nature of transaction data and the labeling process. The transaction
data is peculiar because they are obtained in a streaming fashion, they are
strongly imbalanced and prone to non-stationarity. The labeling is the outcome
of an active learning process, as every day human investigators contact only a
small number of cardholders (associated to the riskiest transactions) and
obtain the class (fraud or genuine) of the related transactions. An adequate
selection of the set of cardholders is therefore crucial for an efficient fraud
detection process. In this paper, we present a number of active learning
strategies and we investigate their fraud detection accuracies. We compare
different criteria (supervised, semi-supervised and unsupervised) to query
unlabeled transactions. Finally, we highlight the existence of an
exploitation/exploration trade-off for active learning in the context of fraud
detection, which has so far been overlooked in the literature
Unsupervised Heterogeneous Coupling Learning for Categorical Representation.
Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. Deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represents heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics
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
ADBench: Anomaly Detection Benchmark
Given a long list of anomaly detection algorithms developed in the last few
decades, how do they perform with regard to (i) varying levels of supervision,
(ii) different types of anomalies, and (iii) noisy and corrupted data? In this
work, we answer these key questions by conducting (to our best knowledge) the
most comprehensive anomaly detection benchmark with 30 algorithms on 57
benchmark datasets, named ADBench. Our extensive experiments (98,436 in total)
identify meaningful insights into the role of supervision and anomaly types,
and unlock future directions for researchers in algorithm selection and design.
With ADBench, researchers can easily conduct comprehensive and fair evaluations
for newly proposed methods on the datasets (including our contributed ones from
natural language and computer vision domains) against the existing baselines.
To foster accessibility and reproducibility, we fully open-source ADBench and
the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed
alphabetically. Code available at https://github.com/Minqi824/ADBenc
A survey on explainable anomaly detection
NWOAlgorithms and the Foundations of Software technolog
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Empowering Responsible Use of Large Language Models
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty, intellectual property infringements, and privacy leaks. In response to these concerns, this thesis explores approaches to promoting the responsible use of LLMs from both theoretical and empirical perspectives.Three key approaches are presented: (1) Detecting AI-generated Text via Watermarking: We propose a robust and high-quality watermarking method called Unigram-Watermark and introduce a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. Furthermore, we propose PF-Watermark, which achieves the best balance of high detection accuracy and low perplexity. (2) Protecting the Intellectual Property of LLMs: We safeguard the intellectual property of LLMs through novel watermarking techniques designed to prevent model-stealing attacks in both text classification and text generation tasks. (3) Privacy-Preserving LLMs: We employ Confidential Redacted Training (CRT) to train and fine-tune language generation models while protecting sensitive information. In summary, we propose a suite of algorithms and solutions to address LLMs' trending safety, security, and privacy concerns. We hope our studies provide valuable insights for researchers to explore exciting future research solutions that promote responsible AI development and deployment