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Yang Y, Khorshidi HA, Aickelin U. A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems. Front Digit Health 2024; 6:1430245. [PMID: 39131184 PMCID: PMC11310152 DOI: 10.3389/fdgth.2024.1430245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024] Open
Abstract
There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.
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Affiliation(s)
- Yuxuan Yang
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
| | - Hadi Akbarzadeh Khorshidi
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
- Cancer Health Services Research, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Uwe Aickelin
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
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Liu Y, Zhu L, Ding L, Huang Z, Sui H, Wang S, Song Y. Selective ensemble method for anomaly detection based on parallel learning. Sci Rep 2024; 14:1420. [PMID: 38228728 PMCID: PMC10791756 DOI: 10.1038/s41598-024-51849-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/10/2024] [Indexed: 01/18/2024] Open
Abstract
Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.
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Affiliation(s)
- Yansong Liu
- School of Software Engineering, Xi'an Jiao Tong University, Xi'an, China
- School of Intelligent Engineering, Shandong Management University, Jinan, China
| | - Li Zhu
- School of Software Engineering, Xi'an Jiao Tong University, Xi'an, China
| | - Lei Ding
- School of Cyberspace Security, Guangzhou University, Guangzhou, China
| | - Zifeng Huang
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China
| | - He Sui
- College of Aeronautical Engineering, Civil Aviation University of China, Tianjin, 300300, China.
- Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China, Tianjin, 300300, China.
| | - Shuang Wang
- Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China, Tianjin, 300300, China
| | - Yuedong Song
- Shanghai Hua Xun Network Information System Co., Ltd, Shanghai, 200135, China
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Kurt B, Kırkbir İB, Kurt T, Güner A, Uluşahin M. A novel computer based risk prediction model for vocal cord palsy before thyroidectomy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107563. [PMID: 37137220 DOI: 10.1016/j.cmpb.2023.107563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 03/15/2023] [Accepted: 04/19/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Thyroidectomy may be performed for clinical indications that include malignancy, benign nodules or cysts suspicious findings on fine needle aspiration (FNA) biopsy, dyspnea from airway compression or dysphagia from cervical esophageal compression, etc. The incidences of vocal cord palsy (VCP) caused by thyroid surgery were reported to range from 3.4% to 7.2% and 0.2% to 0.9% for temporary and permanent vocal fold palsy respectively which is a serious complication of thyroidectomy that is worrisome for patients. OBJECTIVE Therefore, it is aimed to determine the patients who have the risk of developing vocal cord palsy before thyroidectomy by using machine learning methods in the study. In this way, the possibility of developing palsy can be reduced by applying appropriate surgical techniques to individuals in the high-risk group. METHOD For this aim, 1039 patients with thyroidectomy, between the years 2015 and 2018, have been used from Karadeniz Technical University Medical Faculty Farabi Hospital at the department of general surgery. The clinical risk prediction model was developed using the proposed sampling and random forest classification method on the dataset. CONCLUSION As a result, a novel quite a satisfactory prediction model with 100% accuracy was developed for VCP before thyroidectomy. Using this clinical risk prediction model, physicians can be helped to identify patients at high risk of developing post-operative palsy before the operation.
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Affiliation(s)
- Burçin Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey.
| | - İlknur Buçan Kırkbir
- Faculty of Health Sciences, Department of Public Health Nursing and Institute of Medical Science, Dept. of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Tugba Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Ali Güner
- Faculty of Medicine, Department of General Surgery, Karadeniz Technical University, Trabzon, Turkey
| | - Mehmet Uluşahin
- Faculty of Medicine, Department of General Surgery, Karadeniz Technical University, Trabzon, Turkey
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Modeling the Training Iteration Time for Heterogeneous Distributed Deep Learning Systems. INT J INTELL SYST 2023. [DOI: 10.1155/2023/2663115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Distributed deep learning systems effectively respond to the increasing demand for large-scale data processing in recent years. However, the significant investment in building distributed learning systems with powerful computing nodes places a huge financial burden on developers and researchers. It will be good to predict the precise benefit, i.e., how many times of speedup it can get compared with training on single machine (or a few), before actually building such big learning systems. To address this problem, this paper presents a novel performance model on training iteration time for heterogeneous distributed deep learning systems based on the characteristics of the parameter server (PS) system with bulk synchronous parallel (BSP) synchronization style. The accuracy of our performance model is demonstrated by comparing real measurement results on TensorFlow when training different neural networks with various kinds of hardware testbeds: the prediction accuracy is higher than 90% in most cases.
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El Moutaouakil K, Roudani M, El Ouissari A. Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE). Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Distance-based arranging oversampling technique for imbalanced data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07828-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Wei G, Mu W, Song Y, Dou J. An improved and random synthetic minority oversampling technique for imbalanced data. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A Highly Adaptive Oversampling Approach to Address the Issue of Data Imbalance. COMPUTERS 2022. [DOI: 10.3390/computers11050073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling. In the last decade, several sampling methods have been published to address the shortcomings of the initial ones, such as noise sensitivity and incorrect neighbor selection. Based on the review of the literature, it has become clear to us that the algorithms achieve varying performance on different data sets. In this paper, we present a new oversampler that has been developed based on the key steps and sampling strategies identified by analyzing dozens of existing methods and that can be fitted to various data sets through an optimization process. Experiments were performed on a number of data sets, which show that the proposed method had a similar or better effect on the performance of SVM, DTree, kNN and MLP classifiers compared with other well-known samplers found in the literature. The results were also confirmed by statistical tests.
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Santos MS, Abreu PH, Japkowicz N, Fernández A, Soares C, Wilk S, Santos J. On the joint-effect of class imbalance and overlap: a critical review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10150-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets. ELECTRONICS 2022. [DOI: 10.3390/electronics11020228] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.
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Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Zhang X, Zheng Y, Zhao Z, Liu Y, Blumenstein M, Li J. Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Wie YM, Lee KG, Lee KH, Ko T, Lee KH. The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning. MATERIALS 2020; 13:ma13235570. [PMID: 33297369 PMCID: PMC7730768 DOI: 10.3390/ma13235570] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022]
Abstract
The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.
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Affiliation(s)
- Young Min Wie
- Department of Materials Engineering, Kyonggi University, Suwon 16227, Korea; (Y.M.W.); (K.G.L.)
| | - Ki Gang Lee
- Department of Materials Engineering, Kyonggi University, Suwon 16227, Korea; (Y.M.W.); (K.G.L.)
| | - Kang Hyuck Lee
- Center for Built Environment, Sungkyunkwan University, Suwon 16419, Korea;
| | - Taehoon Ko
- Department of Medical Informatics, The Catholic University of Korea, Seoul 06591, Korea;
| | - Kang Hoon Lee
- Department Civil & Environmental Engineering, Hanyang University, Seoul 04763, Korea
- Correspondence: ; Tel.: +82-31-249-9774; Fax: +82-31-244-6300
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