@inproceedings{e5daa8125d17455d90b9a457887b9561,
title = "Weighted ensemble classification of multi-label data streams",
abstract = "Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].",
keywords = "Classification, Data stream, Multi-label",
author = "Lulu Wang and Hong Shen and Hui Tian",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 ; Conference date: 23-05-2017 Through 26-05-2017",
year = "2017",
doi = "10.1007/978-3-319-57529-2\_43",
language = "English",
isbn = "9783319575285",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "551--562",
editor = "Longbing Cao and Kyuseok Shim and Jae-Gil Lee and Jinho Kim and Yang-Sae Moon and Xuemin Lin",
booktitle = "Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings",
address = "Germany",
}