@inproceedings{a5c784d95dbf42a19ca7f3c283329003,
title = "Best first over-sampling for multilabel classification",
abstract = "Learning from imbalanced multilabel data is a challenging task. It has attracted considerable attention recently. In this paper we propose a MultiLabel Best First Over-sampling (ML-BFO) to improve the performance of multilabel classification algorithms, based on imbalance minimization and Wilson's ENN rule. Our experimental results show that ML-BFO not only duplicates fewer samples but also reduces the imbalance level much more than two state-of-the-art multilabel sampling methods, i.e., an over-sampling method LP-ROS and an under-sampling method MLeNN. Besides, ML-BFO significantly improves the performance of multilabel classification algorithms, and performs much better than LP-ROS and MLeNN.",
keywords = "Heuristic, Multilabel learning, Resampling",
author = "Xusheng Ai and Jian Wu and Sheng, {Victor S.} and Yufeng Yao and Pengpeng Zhao and Zhiming Cui",
year = "2015",
month = oct,
day = "17",
doi = "10.1145/2806416.2806634",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "1803--1806",
booktitle = "CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management",
note = "24th ACM International Conference on Information and Knowledge Management, CIKM 2015 ; Conference date: 19-10-2015 Through 23-10-2015",
}