Nonstationary data stream classification with online active learning and siamese neural networks

Abstract

We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.

arXiv commentKeywords: Incremental learning, Active learning, Data streams, Concept drift, Class imbalance

References in corpus (3)

• A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
• Online Learning With Adaptive Rebalancing in Nonstationary Environments
• Queue-based Resampling for Online Class Imbalance Learning

Cited by in corpus (8)

• A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
• Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
• Data augmentation on-the-fly and active learning in data stream classification
• A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
• Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences
• SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks
• Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
• Online Detection of Water Contamination Under Concept Drift