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Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble
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DOI：10.1109/JAS.2022.105518.png)
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Multi-label learning deals with objects associated with multiple class labels, and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance. Since each class might possess its own characteristics, the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning, where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations. As a representative approach, LIFT generates label-specific features by conducting clustering analysis. However, its performance may be degraded due to the inherent instability of the single clustering algorithm. To improve this, a novel multi-label learning approach named SENCE (stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble) is proposed, which stabilizes the generation process of label-specific features via clustering ensemble techniques. Specifically, more stable clustering results are obtained by firstly augmenting the original instance representation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization (EM) algorithm. Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
Keyword:
Clustering ensemble
expectation-maximization algorithm
label-specific features
multi-label learning
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