Online feature selection for high-dimensional class-imbalanced data

• Published in: Knowledge-based systems Vol. 136; pp. 187 - 199
• Main Authors: Zhou, Peng, Hu, Xuegang, Li, Peipei, Wu, Xindong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.11.2017; Elsevier Science Ltd
• Subjects: Algorithms; Class imbalance; Data mining; Datasets; Feature extraction; Fraud; High dimensional; Neighborhood rough set; Online data bases; Online feature selection; Set theory; Online feature selection; Neighborhood rough set; High dimensional; Class imbalance

When tackling high dimensionality in data mining, online feature selection which deals with features flowing in one by one over time, presents more advantages than traditional feature selection methods. However, in real-world applications, such as fraud detection and medical diagnosis, the data is h...