Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

Ksieniewicz, Paweł; Burduk, Robert

doi:10.1007/978-3-030-50423-6_10

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…Ensemble method along with hybrid method is steadily gaining attention from researchers around the world. From 2010 to 2021, there are many literatures regarding research works on highly imbalanced data using ensemble method [15], [61], [62], [63], [64] [65], [66], [67], [68]- [74]. The ensemble technique is popular because it combines many algorithm approaches with the ensemble algorithm in machine learning to improve performance [60], [75]- [77].…”

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

confidence: 99%

“…SVM-SMOTE was used to tackle imbalanced data problems [65]. Using clustering, weighted scoring, SVM, and EM, Ksieniewicz et al suggested a hybrid method for managing severely imbalanced data categorization in geometric space [66]. Using a combination of data rebalancing, bagging-based ensemble learning, and the Extreme Gradient Boosting (XGBoost) algorithm, a unique form of malicious synchrophasors detector was developed to address the highly imbalanced data problem.…”

Section: Ghorbani Et Al Proposed a New Hybrid Model Based On Amentioning

confidence: 99%

“…Some of the literatures were on HIB data and the rest were on HIMC data. The related HIB data was from the data mining domain, and this data was grouped based on these algorithms: SR 0-1 LOSS [15], F-BFR [156], T-BFS [157], K-SUB [64], PSU [102], C4.5N [158], DWCE [159], ECSM [72], GP-COACH [160], Fuzzy [161], [162], OSPREY [163], Chi Method, CNN [164], WL-Norm SVM [165], EAIS + Fuzzy [166], GA + Fuzzy [167], US + Ensemble (Data Hardness) [15], Clustering + WS [66], DBE-DCR [73], EUBoost [168], GA-FS-GL [169], GSVM-RU [170], GA-GL+FRBC [171], K-Means + HFS [172], REPMAC k-Means +SVM + DT [173], SwitchingNED [70], SVM-US [174], B-BFS [67].…”

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

“…Nonetheless, the only research using DL strategy focused on data mining domain. Data Mining C4.5N [158], DWCE [159], ECSM [72], GP-COACH [160], Fuzzy [161], [162], OSPREY [163], Chi Method, CNN [164], WL-Norm SVM [165] Data Mining EAIS + Fuzzy [166], GA + Fuzzy [167], US + Ensemble (Data Hardness) [15], Clustering + WS [66], DBE-DCR [73], EUBoost [168], GA-FS-GL [169], GSVM-RU [170], GA-GL+FRBC [171], K-Means + HFS [172], REPMAC k-Means +SVM + DT [173], SwitchingNED [70], SVM-US [174], B-BFS [67] Data Mining NRA [175] Fraud Detection BERT [19] Malware Detection Ensemble + RF [68] Disease Prediction SSFS [176] Phishing Detection CNN [177], ECDL [178] Medical Imaging DS + ST [179] Hospital Admission…”

Section: A Related Study On Himc Data Frameworkmentioning

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%

Section: Ghorbani Et Al Proposed a New Hybrid Model Based On Amentioning

confidence: 99%

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

Section: A Related Study On Himc Data Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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“…The most commonly used approach to determining the weights uses probability error estimators or other factors [9][10][11]. A distance-weighted approach to calculating the weights is also often used in many problems, were the weights are determined [12][13][14]. In general, this approach is based on the query where the appropriate object is located.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Integration Using Dynamic Regions of Competence

Biedrzycki

Burduk

2020

Entropy

Self Cite

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A vital aspect of the Multiple Classifier Systems construction process is the base model integration. For example, the Random Forest approach used the majority voting rule to fuse the base classifiers obtained by bagging the training dataset. In this paper we propose the algorithm that uses partitioning the feature space whose split is determined by the decision rules of each decision tree node which is the base classification model. After dividing the feature space, the centroid of each new subspace is determined. This centroids are used in order to determine the weights needed in the integration phase based on the weighted majority voting rule. The proposal was compared with other Multiple Classifier Systems approaches. The experiments regarding multiple open-source benchmarking datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use micro and macro-average classification performance measures.

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Clustering and Weighted Scoring Algorithm Based on Estimating the Number of Clusters

Klikowski

Burduk

2021

Computational Science – ICCS 2021

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Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

Cited by 6 publications

References 23 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

Decision Tree Integration Using Dynamic Regions of Competence

Clustering and Weighted Scoring Algorithm Based on Estimating the Number of Clusters

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