KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter

Multi-label classification allows a datapoint to be labelled with more than one class at the same time. In spite of their success 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 a recent 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 article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Experiments described in this article show that KFHE-HOMER performs consistently better than existing multi-label methods including existing approaches based on ensembles.Comment: The paper is under consideration at Pattern Recognition Letters, Elsevie

Benchmarking Multi-label Classification Algorithms

24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 20-21 September 2016Multi-label classification is an approach to classification prob- lems that allows each data point to be assigned to more than one class at the same time. Real life machine learning problems are often multi-label in nature—for example image labelling, topic identification in texts, and gene expression prediction. Many multi-label classification algorithms have been proposed in the literature and, although there have been some benchmarking experiments, many questions still remain about which ap- proaches perform best for certain kinds of multi-label datasets. This pa- per presents a comprehensive benchmark experiment of eleven multi- label classification algorithms on eleven different datasets. Unlike many existing studies, we perform detailed parameter tuning for each algorithm- dataset pair so as to allow a fair comparative analysis of the algorithms. Also, we report on a preliminary experiment which seeks to understand how the performance of different multi-label classification algorithms changes as the characteristics of multi-label datasets are adjusted.Science Foundation Irelan