skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy

Access, IEEE, 2019, Vol.7, p.83396-83411

2013 IEEE ;DOI: 10.1109/ACCESS.2019.2925300

Full text available

Citations Cited by
  • Title:
    Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy
  • Author: Saez, Jose A ; Galar, Mikel ; Krawczyk, Bartosz
  • Subjects: Classification ; Complexity theory ; Computer science ; data generation ; decomposition strategies ; one-vs-one ; overlapping data ; Proposals ; Support vector machines ; Task analysis ; Training
  • Is Part Of: Access, IEEE, 2019, Vol.7, p.83396-83411
  • Description: Learning good-performing classifiers from data with easily separable classes is not usually a difficult task for most of the algorithms. However, problems affecting classifier performance may arise when samples from different classes share similar characteristics or are overlapped , since the boundaries of each class may not be clearly defined. In order to address this problem, the majority of existing works in the literature propose to either adapt well-known algorithms to reduce the negative impact of overlapping or modify the original data by introducing/removing features which decrease the overlapping region. However, these approaches may present some drawbacks: the changes in specific algorithms may not be useful for other methods and modifying the original data can produce variable results depending on data characteristics and the technique used later. An unexplored and interesting research line to deal with the overlapping phenomenon consists of decomposing the problem into several binary subproblems to reduce its complexity, diminishing the negative effects of overlapping. Based on this novel idea in the field of overlapping data, this paper proposes the usage of the One-vs-One ( OVO ) strategy to alleviate the presence of overlapping, without modifying existing algorithms or data conformations as suggested by previous works. To test the suitability of the OVO approach with overlapping data, and due to the lack of proposals in the specialized literature, this research also introduces a novel scheme to artificially induce overlapping in real-world datasets, which enables us to simulate different types and levels of overlapping among the classes. The results obtained show that the methods using the OVO achieve better performances when considering data with overlapped classes than those dealing with all classes at the same time.
  • Publisher: IEEE
  • Language: English
  • Identifier: DOI: 10.1109/ACCESS.2019.2925300
  • Source: IEEE Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait