Classification of Imbalanced Data: Addressing Data Intrinsic Characteristics

Futuristic Trends in Networks and Computing Technologies - Communications in Computer and Information Science ◽

10.1007/978-981-15-4451-4_21 ◽

2020 ◽

pp. 264-277

Author(s):

Armaan Garg ◽

Vishali Aggarwal ◽

Neeti Taneja

Keyword(s):

Imbalanced Data

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An Improved Algorithm for SVMs Classification of Imbalanced Data Sets

Engineering Applications of Neural Networks - Communications in Computer and Information Science ◽

10.1007/978-3-642-03969-0_11 ◽

2009 ◽

pp. 108-118 ◽

Cited By ~ 6

Author(s):

Cristiano Leite Castro ◽

Mateus Araujo Carvalho ◽

Antônio Padua Braga

Keyword(s):

Imbalanced Data ◽

Data Sets ◽

Imbalanced Data Sets ◽

Improved Algorithm

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Classification of wine quality with imbalanced data

2016 IEEE International Conference on Industrial Technology (ICIT) ◽

10.1109/icit.2016.7475021 ◽

2016 ◽

Cited By ~ 6

Author(s):

Gongzhu Hu ◽

Tan Xi ◽

Faraz Mohammed ◽

Huaikou Miao

Keyword(s):

Imbalanced Data ◽

Wine Quality

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Classification of Imbalanced Data Using a Modified Fuzzy-Neighbor Weighted Approach

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2017.0228.07 ◽

2017 ◽

Vol 10 (1) ◽

pp. 56-64 ◽

Cited By ~ 5

Author(s):

Patel Harshita ◽

◽

Thakur Singh ◽

Keyword(s):

Imbalanced Data

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Online sequential classification of imbalanced data by combining extreme learning machine and improved SMOTE algorithm

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280620 ◽

2015 ◽

Cited By ~ 1

Author(s):

Wentao Mao ◽

Jinwan Wang ◽

Liyun Wang

Keyword(s):

Extreme Learning Machine ◽

Imbalanced Data ◽

Sequential Classification ◽

Learning Machine

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Classification of Imbalanced Data by Combining the Complementary Neural Network and SMOTE Algorithm

Lecture Notes in Computer Science - Neural Information Processing. Models and Applications ◽

10.1007/978-3-642-17534-3_19 ◽

2010 ◽

pp. 152-159 ◽

Cited By ~ 44

Author(s):

Piyasak Jeatrakul ◽

Kok Wai Wong ◽

Chun Che Fung

Keyword(s):

Neural Network ◽

Imbalanced Data

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Adaptive Weight Optimization for Classification of Imbalanced Data

Lecture Notes in Computer Science - Intelligence Science and Big Data Engineering ◽

10.1007/978-3-642-42057-3_69 ◽

2013 ◽

pp. 546-553 ◽

Cited By ~ 3

Author(s):

Wenhao Huang ◽

Guojie Song ◽

Man Li ◽

Weisong Hu ◽

Kunqing Xie

Keyword(s):

Imbalanced Data ◽

Weight Optimization ◽

Adaptive Weight

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Minority–Majority Mix mean Oversampling Technique: An Efficient Technique to Improve Classification of Imbalanced Data Sets

Advances in Intelligent Systems and Computing - Computing in Engineering and Technology ◽

10.1007/978-981-32-9515-5_48 ◽

2019 ◽

pp. 501-509

Author(s):

Sachin Patil ◽

Shefali Sonavane

Keyword(s):

Imbalanced Data ◽

Efficient Technique ◽

Data Sets ◽

Imbalanced Data Sets

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Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects

2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) ◽

10.1109/cspa48992.2020.9068669 ◽

2020 ◽

Author(s):

Uzma Batool ◽

Mohd Ibrahim Shapiai ◽

Hilman Fauzi ◽

Jia Xian Fong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Silicon Wafer ◽

Imbalanced Data ◽

Data Classification ◽

Imbalanced Data Classification

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Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks

Engineering Applications of Neural Networks - Communications in Computer and Information Science ◽

10.1007/978-3-319-23983-5_28 ◽

2015 ◽

pp. 304-314 ◽

Cited By ~ 1

Author(s):

Marcelino Lázaro ◽

Francisco Herrera ◽

Aníbal R. Figueiras-Vidal

Keyword(s):

Neural Networks ◽

Imbalanced Data ◽

Bayesian Neural Networks

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LICIC: Less Important Components for Imbalanced Multiclass Classification

Information ◽

10.3390/info9120317 ◽

2018 ◽

Vol 9 (12) ◽

pp. 317 ◽

Cited By ~ 5

Author(s):

Vincenzo Dentamaro ◽

Donato Impedovo ◽

Giuseppe Pirlo

Keyword(s):

Gene Expression ◽

Class Imbalance ◽

Imbalanced Data ◽

Multiclass Classification ◽

Cancer Diagnostics ◽

Mass Spectrometry Data ◽

High Dimensional ◽

Or Gene ◽

High Dimensional Datasets

Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.

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