Improving Anomaly Discovery through Active Learning with Tree-based Ensembles

Core Concepts

Tree-based anomaly detection ensembles are naturally suited for active learning, and the greedy querying strategy of seeking labels for instances with the highest anomaly scores is an efficient approach. Novel batch and streaming active learning algorithms are developed to improve the diversity of discovered anomalies and handle data drift, respectively.

Abstract

The paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles: Provides an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. Presents theoretical analysis to support active anomaly discovery using tree-based ensembles. Develops a novel formalism called compact description (CD) to describe the discovered anomalies using tree-based ensembles. Proposes batch active learning algorithms based on CD to improve the diversity of discovered anomalies. To handle streaming data setting, develops a novel algorithm to robustly detect drift in data streams and design associated algorithms to adapt the anomaly detector on-the-fly in a principled manner. Presents extensive empirical evidence in support of the insights and algorithms on several benchmark datasets. The results show the efficacy of the proposed active learning algorithms in both batch and streaming data settings, discovering significantly more anomalies than state-of-the-art unsupervised baselines.

Stats

The fraction of anomalous data instances is at most τ. The anomaly detection ensemble E has m members. The score matrix for all unlabeled instances is H. The score matrix for positively (negatively) labeled instances is H+ (H-). The weight vector for the scoring function is w. The query budget is B. The labeled dataset is L.

Quotes

"Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances." "Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice." "Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensemble detectors based on label feedback allows us to quickly discover true anomalies."

Key Insights Distilled From

Effectiveness of Tree-based Ensembles for Anomaly Discovery

by Shubhomoy Da... at arxiv.org 04-10-2024

https://arxiv.org/pdf/1901.08930.pdf

Deeper Inquiries

How can the proposed active learning framework be extended to handle high-dimensional or complex data types beyond tabular data

The proposed active learning framework can be extended to handle high-dimensional or complex data types beyond tabular data by incorporating techniques such as feature embedding, dimensionality reduction, and specialized anomaly detection algorithms for specific data types. Feature Embedding: For high-dimensional data, feature embedding techniques like autoencoders or deep learning models can be used to transform the data into a lower-dimensional space while preserving important information. This can help in reducing the complexity of the data and improving the efficiency of the anomaly detection process. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can be applied to reduce the dimensionality of the data while retaining its essential characteristics. This can make the data more manageable for the active learning framework to operate on. Specialized Anomaly Detection Algorithms: Complex data types such as images, text, or time series data may require specialized anomaly detection algorithms tailored to their specific characteristics. For example, convolutional neural networks (CNNs) for image data or recurrent neural networks (RNNs) for time series data can be utilized within the active learning framework to detect anomalies effectively. By integrating these techniques and adapting the active learning framework to accommodate the unique properties of high-dimensional or complex data types, the framework can be extended to handle a broader range of data formats and improve anomaly detection performance.

What are the potential limitations of the compact description approach in capturing diverse anomalies, and how can it be further improved

The compact description approach, while effective in capturing diverse anomalies, may have limitations in scenarios where anomalies exhibit complex patterns or are distributed across multiple subspaces. Some potential limitations of the compact description approach include: Limited Representation: The compact description may not capture all the nuances and variations present in diverse anomalies, especially in cases where anomalies are spread across multiple subspaces or exhibit intricate relationships. Overfitting: There is a risk of overfitting the compact description to the labeled anomalies, which may lead to missing out on detecting novel or unseen anomalies that do not conform to the compact representation. To further improve the compact description approach, the following strategies can be considered: Enhanced Subspace Identification: Develop algorithms that can identify and incorporate additional subspaces or features that may contain diverse anomalies, ensuring a more comprehensive representation. Dynamic Adaptation: Implement mechanisms to dynamically adjust the compact description based on feedback and evolving data patterns, allowing for flexibility in capturing diverse anomalies over time. By addressing these limitations and incorporating adaptive strategies, the compact description approach can be enhanced to better capture diverse anomalies in anomaly detection tasks.

How can the insights and algorithms developed in this work be applied to other machine learning tasks beyond anomaly detection, such as few-shot learning or active learning for classification

The insights and algorithms developed in this work for anomaly detection using tree-based ensembles and active learning can be applied to other machine learning tasks beyond anomaly detection, such as few-shot learning or active learning for classification, in the following ways: Few-Shot Learning: The active learning framework can be adapted for few-shot learning by incorporating strategies to select and label a small number of instances for model training. The insights on efficient query selection and weight updating can help in improving model performance with limited labeled data. Active Learning for Classification: The algorithms developed for updating weights based on label feedback can be utilized in active learning scenarios for classification tasks. By incorporating human feedback to adjust model parameters, the classification model can be fine-tuned and optimized for better performance. Transfer Learning: The principles of ensemble learning and active learning can be applied in transfer learning scenarios to adapt pre-trained models to new tasks or domains. By leveraging the insights on model configuration and feedback incorporation, transfer learning processes can be enhanced for improved generalization and performance. By leveraging the insights and algorithms developed in this work, machine learning tasks beyond anomaly detection can benefit from enhanced learning strategies and improved model adaptability.