Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams

Martha Roseberry, Alberto Cano
Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 94:23-37, 2018.

Abstract

Multi-label data streams is a highly challenging task involving drifts in features and labels. Classifiers must automatically adapt to changes while keeping a competitive accuracy in a real-time dynamic environment where the frequencies of the labelsets are non-stationary and highly imbalanced. This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). It exploits short- and long-term memories to predict the current and evolving states of the data stream. The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. Non-parametric statistical analysis of the results shows the superiority of ML-SAM-kNN, including when compared with ML-kNN.

Cite this Paper

BibTeX
@InProceedings{pmlr-v94-roseberry18a, title = {Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams}, author = {Roseberry, Martha and Cano, Alberto}, booktitle = {Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {23--37}, year = {2018}, editor = {Torgo, Luís and Matwin, Stan and Japkowicz, Nathalie and Krawczyk, Bartosz and Moniz, Nuno and Branco, Paula}, volume = {94}, series = {Proceedings of Machine Learning Research}, month = {10 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v94/roseberry18a/roseberry18a.pdf}, url = {https://proceedings.mlr.press/v94/roseberry18a.html}, abstract = {Multi-label data streams is a highly challenging task involving drifts in features and labels. Classifiers must automatically adapt to changes while keeping a competitive accuracy in a real-time dynamic environment where the frequencies of the labelsets are non-stationary and highly imbalanced. This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). It exploits short- and long-term memories to predict the current and evolving states of the data stream. The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. Non-parametric statistical analysis of the results shows the superiority of ML-SAM-kNN, including when compared with ML-kNN.} }
Endnote
%0 Conference Paper %T Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams %A Martha Roseberry %A Alberto Cano %B Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2018 %E Luís Torgo %E Stan Matwin %E Nathalie Japkowicz %E Bartosz Krawczyk %E Nuno Moniz %E Paula Branco %F pmlr-v94-roseberry18a %I PMLR %P 23--37 %U https://proceedings.mlr.press/v94/roseberry18a.html %V 94 %X Multi-label data streams is a highly challenging task involving drifts in features and labels. Classifiers must automatically adapt to changes while keeping a competitive accuracy in a real-time dynamic environment where the frequencies of the labelsets are non-stationary and highly imbalanced. This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). It exploits short- and long-term memories to predict the current and evolving states of the data stream. The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. Non-parametric statistical analysis of the results shows the superiority of ML-SAM-kNN, including when compared with ML-kNN.
APA
Roseberry, M. & Cano, A.. (2018). Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams. Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 94:23-37 Available from https://proceedings.mlr.press/v94/roseberry18a.html.

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