Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification

TL;DR

This paper introduces ML-BELS, a neural network-based multi-label stream classification model that balances effectiveness and efficiency, handles concept drift, and manages missing labels through a novel weighted ensemble approach.

Contribution

The paper presents a new weighted ensemble method, ML-BELS, that improves multi-label stream classification by balancing accuracy, efficiency, and robustness to missing labels and concept drift.

Findings

ML-BELS outperforms 11 state-of-the-art baselines across multiple datasets.

The model effectively handles concept drift and missing labels in non-stationary environments.

Using label cardinality as a trigger improves model accuracy in low-label-cardinality datasets.

Abstract

Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot balance effectiveness and efficiency. In this work, we propose a neural network-based approach that tackles this issue and is suitable for high-dimensional multi-label classification. Our model uses a selective concept drift adaptation mechanism that makes it suitable for a non-stationary environment. Additionally, we adapt our model to an environment with missing labels using a simple yet effective imputation strategy and demonstrate that it outperforms a vast majority of the state-of-the-art supervised models. To achieve our purposes, we introduce a weighted binary relevance-based approach named ML-BELS using the Broad Ensemble Learning System (BELS) as its base classifier. Instead of a chain of stacked classifiers, our model employs independent weighted ensembles, with the weights generated by the predictions of a BELS classifier. We show that using the weighting strategy on datasets with low label cardinality negatively impacts the accuracy of the model; with this in mind, we use the label cardinality as a trigger for applying the weights. We present an extensive assessment of our model using 11 state-of-the-art baselines, five synthetics, and 13 real-world datasets, all with different characteristics. Our results demonstrate that the proposed approach ML-BELS is successful in balancing effectiveness and efficiency, and is robust to missing labels and concept drift.