On the Use of Surrounding Neighbors for Synthetic Over-Sampling of the Minority Class

被引：0
|
作者：
Garcia, V. [1 ]
Sanchez, J. S. [1 ]
Mollineda, R. A. [1 ]
机构：
[1] Univ Jaume 1, Dept Llenguatges & Sistemes Informat, Av Sos Baynat S-N, Castellon de La Plana 12071, Spain
来源：
SMO 08: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SIMULATION, MODELLING AND OPTIMIZATION | 2008年
关键词：
Imbalance; Over-sampling; Surrounding neighborhood; SMOTE; Proximity graph; k-NCN;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
It has been observed that class imbalance may produce an important deterioration of the classification accuracy. One of the most popular methods to tackle this problem is the synthetic minority over-sampling technique (SMOTE). From the original SMOTE algorithm, we here propose the use of three surrounding neighborhood approaches with the aim of generating artificial minority examples, but taking both the proximity and the spatial distribution of the examples into account. Experiments with ten real data sets are conducted to compare the models introduced in this paper with SMOTE, demonstrating their effectiveness in a number of problems.
引用
收藏
页码：389 / +
页数：2
相关论文
共 50 条
  • [1] SMOTE: Synthetic minority over-sampling technique
    Chawla, Nitesh V.
    Bowyer, Kevin W.
    Hall, Lawrence O.
    Kegelmeyer, W. Philip
    2002, American Association for Artificial Intelligence (16):
  • [2] Transfer synthetic over-sampling for class-imbalance learning with limited minority class data
    Xu-Ying Liu
    Sheng-Tao Wang
    Min-Ling Zhang
    Frontiers of Computer Science, 2019, 13 : 996 - 1009
  • [3] Transfer synthetic over-sampling for class-imbalance learning with limited minority class data
    Liu, Xu-Ying
    Wang, Sheng-Tao
    Zhang, Min-Ling
    FRONTIERS OF COMPUTER SCIENCE, 2019, 13 (05) : 996 - 1009
  • [4] Safe Level Graph for Synthetic Minority Over-sampling Techniques
    Bunkhumpornpat, Chumphol
    Subpaiboonkit, Sitthichoke
    2013 13TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT): COMMUNICATION AND INFORMATION TECHNOLOGY FOR NEW LIFE STYLE BEYOND THE CLOUD, 2013, : 570 - 575
  • [5] DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
    Bunkhumpornpat, Chumphol
    Sinapiromsaran, Krung
    Lursinsap, Chidchanok
    APPLIED INTELLIGENCE, 2012, 36 (03) : 664 - 684
  • [6] RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem
    Soltanzadeh, Paria
    Hashemzadeh, Mahdi
    INFORMATION SCIENCES, 2021, 542 : 92 - 111
  • [7] DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
    Chumphol Bunkhumpornpat
    Krung Sinapiromsaran
    Chidchanok Lursinsap
    Applied Intelligence, 2012, 36 : 664 - 684
  • [8] Boosting the performance of over-sampling algorithms through under-sampling the minority class
    de Morais, Romero F. A. B.
    Vasconcelos, Germano C.
    NEUROCOMPUTING, 2019, 343 : 3 - 18
  • [9] Article FLEX-SMOTE: Synthetic over-sampling technique that flexibly adjusts to different minority class distributions
    Bunkhumpornpat, Chumphol
    Boonchieng, Ekkarat
    Chouvatut, Varin
    Lipsky, David
    PATTERNS, 2024, 5 (11):
  • [10] Multi-fidelity model based on synthetic minority over-sampling technique
    Jiuxiang Song
    Jizhong Liu
    Multimedia Tools and Applications, 2024, 83 : 33123 - 33139
12345