A multiclass classification using one-versus-all approach with the differential partition sampling ensemble. (January 2021)

• Record Type: Journal Article
• Authors: Gao, Xin; He, Yang; Zhang, Mi; Diao, Xinping; Jing, Xiao; Ren, Bing; Ji, Weijia
• Is Part Of: Engineering applications of artificial intelligence. Volume 97(2021)
• Publication Date: 2021-01
• Subjects: Multiclass classification -- One-versus-all -- Class imbalance -- Differential partition sampling -- Ensemble learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104034
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The One-versus-all(OVA) approach is one of the mainstream decomposition methods by which multiple binary classifiers are used to solve multiclass classification tasks. However, it exists the problems of serious class imbalance. This paper proposes a differential partition sampling ensemble method(DPSE) in the OVA framework. The number of majority samples and that of the minority samples in each binary training dataset are used as the upper and lower limits of the sampling interval respectively. Within this range, the construction process of the arithmetic sequence is simulated to generate the set containing multiple different sampling numbers with equal intervals. All samples are divided into safe examples, borderline examples, rare examples, and outliers according to the neighborhood information, then Random undersampling for safe samples(s-Random undersampling) and SMOTE for borderline examples and rare examples (br-SMOTE) are proposed based on the distribution characteristics of the classes. In each iteration, according to the number of differential sampling, the two methods are used to undersample or oversample the majority and minority in each binary training dataset to balance the number of positive and negative samples, which preserves the characteristic of the class structure as much as possible. Balanced training sets are used to train the binary classification model with multiple sub classifiers. The thorough experiments performed on 27 KEEL public multiclass datasets show that DPSE outperforms the typical methods in the OVA scheme, the One-versus-One scheme or direct way in classification performance.