An under-sampling method based on fuzzy logic for large imbalanced dataset

Abstract Background The incidence of cardiac implantable electronic device infection (CIEDI) is low and usually belongs to the typical imbalanced dataset. We sought to describe our experience on the management of the imbalanced CIEDI dataset. Methods Database from two centers of patients undergoing device implantation from 2001 to 2016 were reviewed retrospectively. Re-sampling technique was used to improve the classifier accuracy. Results CIEDI was identified in 28 out of 4959 procedures (0.56%); a high imbalance existed in the sizes of the patient profiles. In univariate analyses, replacement procedure and male were significantly associated with an increase in CIEDI: (53.6% vs. 23.4, 0.8% vs. 0.3%, P < 0.01). Multivariate logistic regression analysis showed that gender (odds ratio, OR = 3.503), age (OR = 1.032), replacement procedure (OR = 3.503), and use of antibiotics (OR = 0.250) remained as independent predictors of CIEDI (all P < 0.05) after adjustment for diabetes, post-operation fever, and device style, device company. There were 616 under-sampled cases and 123 over-sampled cases in the analyzed cohort after re-sampling. The re-sampling and bootstrap results were robust and largely like the analysis results prior re-sampling method, while use of antibiotics lost the predicting capacity for CIEDI after re-sampling technique (P > 0.05). Conclusion The application of re-sampling techniques can generate useful synthetic samples for the classification of imbalanced data and improve the accuracy of predicting efficacy of CIEDI. The peri-operative assessment should be intensified in male and aged patients as well as patients receiving replacement procedures for the risk of CIEDI.

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A New Under-Sampling Method Using Genetic Algorithm for Imbalanced Data Classification

Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication - IMCOM '16 ◽

10.1145/2857546.2857643 ◽

2016 ◽

Cited By ~ 9

Author(s):

Jihyun Ha ◽

Jong-Seok Lee

Keyword(s):

Genetic Algorithm ◽

Sampling Method ◽

Imbalanced Data ◽

Data Classification ◽

Imbalanced Data Classification ◽

Under Sampling

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CoGBUS- Center of Gravity based under Sampling Method for Imbalanced Data Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2077.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 2463-2468

Keyword(s):

Learning Community ◽

Sampling Method ◽

Class Imbalance ◽

Imbalanced Data ◽

Center Of Gravity ◽

Classification Algorithms ◽

Class Imbalance Problem ◽

Imbalance Problem ◽

Imbalanced Data Classification ◽

Under Sampling

Learning of class imbalanced data becomes a challenging issue in the machine learning community as all classification algorithms are designed to work for balanced datasets. Several methods are available to tackle this issue, among which the resampling techniques- undersampling and oversampling are more flexible and versatile. This paper introduces a new concept for undersampling based on Center of Gravity principle which helps to reduce the excess instances of majority class. This work is suited for binary class problems. The proposed technique –CoGBUS- overcomes the class imbalance problem and brings best results in the study. We take F-Score, GMean and ROC for the performance evaluation of the method.

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An Under-Sampling Method of Unbalanced Data Classification Based on Support Vector

Human Centered Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-70626-5_45 ◽

2021 ◽

pp. 430-440

Author(s):

Jinqiang He ◽

Yongli Liao ◽

Dengjie Zhu ◽

Xujuan Fan ◽

Xiancong Zhang

Keyword(s):

Sampling Method ◽

Data Classification ◽

Support Vector ◽

Unbalanced Data ◽

Under Sampling

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The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction

JUITA Jurnal Informatika ◽

10.30595/juita.v9i1.9969 ◽

2021 ◽

Vol 9 (1) ◽

pp. 131

Author(s):

Binti Solihah ◽

Azhari Azhari ◽

Aina Musdholifah

Keyword(s):

Decision Tree ◽

B Cell ◽

Sampling Method ◽

Prediction Models ◽

Learning Algorithm ◽

Class Imbalance ◽

Epitope Prediction ◽

Conformational Epitope ◽

Ensemble Model ◽

Under Sampling

A conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell prediction. In this paper, we compare several conformational epitope B cell prediction models from non-ensemble and ensemble approaches. A sampling method from Random undersampling, SMOTE, and cluster-based undersampling is combined with a decision tree or SVM to build a non-ensemble model. A random forest model and several variants of the bagging method is used to construct the ensemble model. A 10-fold cross-validation method is used to validate the model. The experiment results show that the combination of the cluster-based under-sampling and decision tree outperformed the other sampling method when combined with the non-ensemble and the ensemble method. This study provides a baseline to improve existing models for dealing with the class imbalance in the conformational epitope prediction.

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