Data-efficient Online Classification with Siamese Networks and Active Learning

TL;DR

This paper introduces a novel online classification method combining Siamese networks and active learning, effectively handling limited, imbalanced, and nonstationary data streams with minimal labeled data.

Contribution

It proposes a robust, data-efficient online learning approach that outperforms existing methods in speed and accuracy using a multi-sliding window and class-balanced queues.

Findings

Outperforms baselines and state-of-the-art algorithms

Effective with only 1% labeled data

Robust to data nonstationarity and imbalance

Abstract

An ever increasing volume of data is nowadays becoming available in a streaming manner in many application areas, such as, in critical infrastructure systems, finance and banking, security and crime and web analytics. To meet this new demand, predictive models need to be built online where learning occurs on-the-fly. Online learning poses important challenges that affect the deployment of online classification systems to real-life problems. In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification. We propose a learning method that synergistically combines siamese neural networks and active learning. The proposed method uses a multi-sliding window approach to store data, and maintains separate and balanced queues for each class. Our study shows that the proposed method is robust to data nonstationarity and imbalance, and significantly outperforms baselines and state-of-the-art algorithms in terms of both learning speed and performance. Importantly, it is effective even when only 1% of the labels of the arriving instances are available.