Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem

研究成果: Conference contribution同行評審

8 引文 斯高帕斯(Scopus)

摘要

The imbalanced data problem occurs in many application domains and is considered to be a challenging problem in machine learning and data mining. Most resampling methods for synthetic data focus on minority class without considering the data distribution of major classes. In contrast to previous works, the proposed method considers both majority classes and minority classes to learn feature embeddings and utilizes appropriate loss functions to make feature embedding as discriminative as possible. The proposed method is a comprehensive framework and different deep learning feature extractors can be utilized for different domains. We conduct experiments utilizing seven numerical datasets and one image dataset based on multiclass classification tasks. The experimental results indicate that the proposed method provides accurate and stable results.

原文English
主出版物標題2018 IEEE International Conference on Data Mining, ICDM 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1146-1151
頁數6
ISBN(電子)9781538691588
DOIs
出版狀態Published - 27 12月 2018
事件18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
持續時間: 17 11月 201820 11月 2018

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2018-November
ISSN(列印)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
國家/地區Singapore
城市Singapore
期間17/11/1820/11/18

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