Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets. Two variants are introduced based on the underlying component classifier algorithm, ML-KFHE-HOMER, and ML-KFHE-CC which uses HOMER and Classifier Chain (CC) as the underlying multi-label algorithms respectively. ML-KFHE-HOMER and ML-KFHE-CC sequentially train multiple HOMER and CC multi-label classifiers and aggregate their outputs using the sensor fusion properties of the Kalman filter. Extensive experiments and detailed analysis were performed on thirteen multi-label datasets and eight other algorithms, which included state-of-the-art ensemble methods. The results show, for both versions, the ML-KFHE framework improves the predictive performance significantly with respect to bagged combinations of HOMER (named E-HOMER), also introduced in this paper, and bagged combination of CC, Ensemble Classifier Chains (ECC), thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHE-HOMER variant was found to perform consistently and significantly better than the compared multi-label methods including existing approaches based on ensembles.

翻译：尽管集成分类方法在多类分类问题中取得了成功，但基于bagging以外方法的集成技术在多标签分类问题中尚未得到广泛探索。基于卡尔曼滤波器的启发式集成（KFHE）是一种利用卡尔曼滤波器的传感器融合特性来组合多个分类器模型的集成方法，已被证明非常有效。本文提出了KFHE的多标签版本ML-KFHE，论证了KFHE方法在多标签数据集上的有效性。根据底层组件分类算法的不同，引入了两种变体：ML-KFHE-HOMER和ML-KFHE-CC，分别使用HOMER和分类器链（CC）作为底层多标签算法。ML-KFHE-HOMER和ML-KFHE-CC依次训练多个HOMER和CC多标签分类器，并利用卡尔曼滤波器的传感器融合特性对其输出进行聚合。在十三个多标签数据集上与八种其他算法（包括最新的集成方法）进行了大量实验和详细分析。结果表明，对于两种版本，ML-KFHE框架相比HOMER的bagging组合（本文亦称为E-HOMER）和CC的bagging组合（集成分类器链，ECC），显著提升了预测性能，从而证明了ML-KFHE的有效性。此外，ML-KFHE-HOMER变体被发现持续且显著优于比较的多标签方法，包括现有基于集成的方法。