Hoeffding adaptive trees for multi-label classification on data streams

TL;DR

This paper introduces MLHAT, a novel multi-label data stream classifier using Hoeffding adaptive trees that effectively handles concept drift, label relations, and class imbalance, outperforming existing methods on multiple datasets.

Contribution

The paper presents MLHAT, the first multi-label Hoeffding adaptive tree that dynamically adapts to concept drift and label relations in data streams, improving classification performance.

Findings

MLHAT outperforms 18 state-of-the-art classifiers on 41 datasets.

MLHAT achieves significant improvements in 12 multi-label metrics.

The approach effectively detects and adapts to concept drift in multi-label streams.

Abstract

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream environments where instances have the peculiarity of belonging simultaneously to more than one class, the problem becomes even more complex and poses unique challenges such as different concept drifts impacting different labels at simultaneous or distinct times, higher class imbalance, or new labels emerging in the stream. This paper proposes a novel approach to multi-label data stream classification called Multi-Label Hoeffding Adaptive Tree (MLHAT). MLHAT leverages the Hoeffding adaptive tree to address these challenges by considering possible relations and label co-occurrences in the partitioning process of the decision tree, dynamically adapting the learner in each leaf node of the tree, and implementing a concept drift detector that can quickly detect and replace tree branches that are no longer performing well. The proposed approach is compared with other 18 online multi-label classifiers on 41 datasets. The results, validated with statistical analysis, show that MLHAT outperforms other state-of-the-art approaches in 12 well-known multi-label metrics.