@inproceedings{f3a1ec8408414ce9be32d1314a7f9ae3,
title = "Self-paced learning for imbalanced data",
abstract = "In this paper, we propose a novel training paradigm that combines two learning strategies: cost-sensitive and self-paced learning. This learning approach can be applied to the decision problems where highly imbalanced data is used during training process. The main idea behind the proposed method is to start the learning process by taking large number of minority examples and only the easiest majority objects and then gradually turning to more difficult cases. We examine the quality of this training paradigm comparing to other learning schemas for neural network model using a set of highly imbalanced benchmark datasets.",
keywords = "Cost-sensitive learning, Imbalanced data, Self-paced learning",
author = "Maciej Zi{\c e}ba and Tomczak, {Jakub M.} and Jerzy {\'S}wi{\c a}tek",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-662-49381-6_54",
language = "English",
isbn = "9783662493809",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "564--573",
editor = "Nguyen, {Ngoc Thanh} and Bogdan Trawinski and Tzung-Pei Hong and Hamido Fujita",
booktitle = "Intelligent Information and Database Systems - 8th Asian Conference, ACIIDS 2016, Proceedings",
address = "Germany",
note = "8th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2016 ; Conference date: 14-03-2016 Through 16-03-2016",
}