A Review on Solution to Class Imbalance Problem: Undersampling Approaches

Devi, Debashree; Biswas, Saroj; Purkayastha, Biswajit

doi:10.1109/compe49325.2020.9200087

Cited by 39 publications

(33 citation statements)

References 26 publications

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“…However, there are issues on overlapping class and noisy data [21], [33], [155]. A good solution for the problem is to combine a method that can minimize overlapping class and reduce noisy data [2], [31], [200]. A promising solution that might handle both problems is by using ensemble methods.…”

Section: Discussionmentioning

confidence: 99%

“…When compared to the implementation in a single classifier, the ensemble's total performance is improved by combining several approaches or algorithms. There is a lot of literature stating about the performance regarding ensemble method [1], [2], [125], [129]- [132].…”

Section: A Related Method/technique In Handling Highly Imbalanced Mul...mentioning

confidence: 99%

“…Another solution to overcome the problem is by creating synthetic data to move overlapping data to new spaces [38]. Another method is to remove excessive samples and maintain the quality of the data [2], [6] [1], [6].…”

Section: Highly Imbalanced Multi-class (Himc) Datamentioning

confidence: 99%

“…Relationship among classes is an issue in multi-class data. It is a complicated situation as each group of classes presents different problems to the data [1], [2], [4], [38], [39]. This problem is elevated in HIMC data, and affects classifiers' performance [40], [41].…”

Section: Highly Imbalanced Multi-class (Himc) Datamentioning

confidence: 99%

“…Every single piece of information in this world is data. The nature of data is that it is not absolutely balanced [1], [2]. It is either slightly imbalanced or highly imbalanced.…”

Section: Introductionmentioning

confidence: 99%

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Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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Section: Discussionmentioning

confidence: 99%

Section: A Related Method/technique In Handling Highly Imbalanced Mul...mentioning

confidence: 99%

Section: Highly Imbalanced Multi-class (Himc) Datamentioning

confidence: 99%

Section: Highly Imbalanced Multi-class (Himc) Datamentioning

confidence: 99%

“…Every single piece of information in this world is data. The nature of data is that it is not absolutely balanced [1], [2]. It is either slightly imbalanced or highly imbalanced.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

Fanizzi,

Arezzo,

Cormio

et al. 2024

Cancer Medicine

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BackgroundAccurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black‐boxes due to the difficulty of understanding the decision‐making process used by the algorithm to obtain a specific result.AimsFor this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis.Materials & MethodsSince the diagnostic task was a three‐class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme.ResultsThe accuracy of the three‐class model reaches an overall accuracy of 86.36%, and the precision per‐class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system.ConclusionsThis is the first work that attempts to design an explainable machine‐learning tool for the histological diagnosis of solid masses of the ovary.

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A transfer weighted extreme learning machine for imbalanced classification

Guo

Jiao

Tan

et al. 2022

Int J of Intelligent Sys

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Previous class imbalance learning methods are mostly grounded on the assumption that all training data have been labeled, however, is impractical in many real-world applications. The limited amount of labeled instances may produce a classifier with poor generalization. To address the issue, a transfer weighted extreme learning machine (TWELM) classifier is proposed, with the purpose of extracting knowledge from other domains to improve the classification performance of a classifier in a limited labeled target domain. To be specific, a well-tuned weighted extreme learning machine classifier is first learned from source data that has been completely labeled. Subsequently, another extreme learning machine classifier is obtained from the limited labeled target domain data to preserve the target domain structural knowledge and the decision boundary information. Finally, the target classifier

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A Review on Solution to Class Imbalance Problem: Undersampling Approaches

Cited by 39 publications

References 26 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

A transfer weighted extreme learning machine for imbalanced classification

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