An under-sampling technique for imbalanced data classification based on DBSCAN algorithm

Mirzaei, Behzad; Nikpour, Bahareh; Nezamabadi‐pour, Hossein

doi:10.1109/cfis49607.2020.9238718

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…There are many literatures on the performance of ensemble method. Among more prevalent methods are Bagging (Bootstrap aggregation) [6], [28] [96]- [99], [100], Stacking (Stacked Generalization) [98], [101], [102], Random Forest (RF) [87], [91], [103], [13], [104]- [109] and Mixtures of Experts [8], [42], [110], [111], [112], [113].…”

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

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

confidence: 99%

“…It also can happen when the dataset is skewed [12]. Most classifiers in a balanced class are biased toward the majority class [13], [14], [15]. In real life, all real-world data is imbalanced [16], [17].…”

Section: A Imbalanced Datamentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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“…The experimental results indicate that the classification performance are improved effectively when the DBN-based ensemble strategy is integrated with over-sampling techniques. Mirzaei, Nikpour & Nezamabadi-Pour (2020) present an effective under-sampling technique to select the suitable samples of majority class using the DBSCAN algorithm. The results of balancing training sets show that this method is superior to other six pretreatment methods.…”

Section: Related Workmentioning

confidence: 99%

A multi-level classification based ensemble and feature extractor for credit risk assessment

Wang,

Wu,

Gao

et al. 2024

PeerJ Computer Science

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With the growth of people’s demand for loans, banks and other financial institutions put forward higher requirements for customer credit risk level classification, the purpose is to make better loan decisions and loan amount allocation and reduce the pre-loan risk. This article proposes a Multi-Level Classification based Ensemble and Feature Extractor (MLCEFE) that incorporates the strengths of sampling, feature extraction, and ensemble classification. MLCEFE uses SMOTE + Tomek links to solve the problem of data imbalance and then uses a deep neural network (DNN), auto-encoder (AE), and principal component analysis (PCA) to transform the original variables into higher-level abstract features for feature extraction. Finally, it combined multiple ensemble learners to improve the effect of personal credit risk multi-classification. During performance evaluation, MLCEFE has shown remarkable results in the multi-classification of personal credit risk compared with other classification methods.

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