Synthetic Minority Over-Sampling Technique based on Fuzzy C-means Clustering for Imbalanced Data

被引：0

作者：

Lee, Hansoo ^{[1
]}

Jung, Seunghyan ^{[1
]}

Kim, Minseok ^{[1
]}

Kimt, Sungshin ^{[2
]}

机构：

[1] Pusan Natl Univ, Dept Elect & Comp Engn, Busan 46241, South Korea

[2] Pusan Natl Univ, Sch Elect & Comp Engn, Busan 46241, South Korea

来源：

2017 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY) | 2017年

关键词：

Synthetic minority over-sampling technique; fuzzy c-means clustering; extended gap statistics index; class imbalance problem; machine learning; SMOTE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Some solutions to solve the class imbalance problem that is one of the representative difficulties in machine learning have been proposed. Among them, SMOTE algorithm is proposed recently to reduce the influence of the problem and shows its remarkable performance to solve real world problems. This paper proposes a novel kind of the SMOTE algorithm by combining the previous SMOTE algorithm and fuzzy logic to deal with uncertainties underlying learning samples. Also, fuzzy c-means clustering is used to assign membership degree to given samples efficiently. Moreover, the extended gap statistics is applied to select the optimal number of cluster. The proposed algorithm is evaluated on using several benchmark datasets and shows its good performance by combining support vector machine classifier.

引用

页数：6

共 50 条

[1] Imbalanced data classification using improved synthetic minority over-sampling technique
Anusha, Yamijala
Visalakshi, R.
Srinivas, Konda
[J]. MULTIAGENT AND GRID SYSTEMS, 2023, 19 (02) : 117 - 131
[2] Handling Autism Imbalanced Data using Synthetic Minority Over-Sampling Technique (SMOTE)
El-Sayed, Asmaa Ahmed
Meguid, Nagwa Abdel
Mahmood, Mahmood Abdel Manem
Hefny, Hesham Ahmed
[J]. PROCEEDINGS OF 2015 THIRD IEEE WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS), 2015,
[3] Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data
Feng, Wei
Dauphin, Gabriel
Huang, Wenjiang
Quan, Yinghui
Bao, Wenxing
Wu, Mingquan
Li, Qiang
[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (07) : 2159 - 2169
[4] AN IMBALANCED SIGNAL MODULATION CLASSIFICATION AND EVALUATION METHOD BASED ON SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE
Liu, Xuebo
Wang, Yiran
Bai, Jing
Li, Haoran
Wang, Xu
[J]. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 6224 - 6227
[5] Imbalanced Data Over-Sampling Method Based on ISODATA Clustering
Lv, Zhenzhe
Liu, Qicheng
[J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2023, E106D (09) : 1528 - 1536
[6] An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data
Hao, Ming
Wang, Yanli
Bryant, Stephen H.
[J]. ANALYTICA CHIMICA ACTA, 2014, 806 : 117 - 127
[7] SMOTE: Synthetic minority over-sampling technique
Chawla, Nitesh V.
Bowyer, Kevin W.
Hall, Lawrence O.
Kegelmeyer, W. Philip
[J]. 2002, American Association for Artificial Intelligence (16):
[8] Construction of EBRB classifier for imbalanced data based on Fuzzy C-Means clustering
Fu, Yang-Geng
Ye, Ji-Feng
Yin, Ze-Feng
Chen, Long-Jiang
Wang, Ying-Ming
Liu, Geng-Geng
[J]. KNOWLEDGE-BASED SYSTEMS, 2021, 234
[9] Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique
Eom, Gayeong
Byeon, Haewon
[J]. MATHEMATICS, 2023, 11 (16)
[10] Classification of imbalanced PubChem BioAssay data using an efficient algorithm coupled with synthetic minority over-sampling technique
Hao, Ming
Wang, Yanli
Bryant, Stephen H.
[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2014, 247

← 1 2 3 4 5 →