HAR-MI method for multi-class imbalanced datasets

Hartono, H.; Risyani, Yeni; Ongko, Erianto; Abdullah, Dahlan

doi:10.12928/telkomnika.v18i2.14818

Cited by 5 publications

(5 citation statements)

References 21 publications

(27 reference statements)

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“…Unlike previous methods, this method uses randomly generated priors for sampling instead of class ratios. Hartono et al [15] combined dynamic ensemble selection with MultiRandBal in their HAR-MI method, maintaining the diversity of data and classifiers, and achieving higher performance with a small number of classifiers. In this paper, we intend to borrow the idea of random balance strategy to determine the number of samples per class based on the class proportion of each data chunk.…”

Section: Multi-class Imbalance Data Classification Methodsmentioning

confidence: 99%

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Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Han

Gao

et al. 2023

Applied Sciences

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The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm deals with imbalance and concept drift problems through the hybrid sampling and dynamic weighting phases, respectively. In the hybrid sampling phase, adaptive spectral clustering is proposed to sample the data after clustering, which can maintain the original data distribution; then the sample safety factor is used to determine the samples to be sampled for each class; the safe samples are oversampled and the unsafe samples are under-sampled in each cluster. If the data stream is extremely imbalanced, the sample storage pool is used to extract samples with a high safety factor to add to the data stream. In the dynamic weighting phase, a dynamic weighting method based on the G-mean value is proposed. The G-mean values are used as the weights of each base classifier in the ensemble and the ensemble is dynamically updated during the processing of the data stream to accommodate the occurrence of concept drift. Experiments were conducted with LB, OAUE, ARF, BOLE, MUOB, MOOD, CALMID, and the proposed HSDW-MI on 10 multi-class synthetic data streams with different class ratios and concept drifts and 3 real multi-class imbalanced streams with unknown drifts, and the results show that the proposed HSDW-MI has better classification capabilities and performs more consistently compared to all other algorithms.

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Section: Multi-class Imbalance Data Classification Methodsmentioning

confidence: 99%

“…The F1-score is the harmonic mean of Recall and Precision, which tries to balance these two metrics and does not ignore the majority class samples. Additionally, in this paper, the macro-F1-score is used as the evaluation metric of the algorithm, as shown in Equation (15).…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Han

Gao

et al. 2023

Applied Sciences

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“…The algorithm of hybrid approach redefinition for multi-class imbalance is being as [21]. Based on the preceding algorithm, it is clear that the HAR-MI method is divided into 2 (two) major stages: preprocessing and processing.…”

Section: Hybrid Approach Redefinition For Multi-class Imbalancementioning

confidence: 99%

“…The hybrid ensembles approach that combines the application of preprocessing by using feature selection and sampling at the processing stage is hybrid approach redefinition-multiclass imbalance [21]. The hybrid approach redefinition-multi class (HAR-MI) approach will be combined with the resampling algorithm in the processing stage and feature selection in the preprocessing stage in this analysis.…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

Ongko

Hartono

2021

Bulletin EEI

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Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.

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“…The evaluation metrics of the ten folds are averaged to verify the superiority of the proposed ensemble classifier. As the Wilcoxon signed-rank test was widely used in machine learning literature [50][51][52], it is done on all three datasets to examine if the new method was statistically better as compared to single methods and check if the contribution of the Relief algorithm is significant.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang²

2020

Preprint

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Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

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HAR-MI method for multi-class imbalanced datasets

Cited by 5 publications

References 21 publications

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

A hybrid cost-sensitive ensemble for heart disease prediction

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