Random Balance ensembles for multiclass imbalance learning

Rodrı́guez, Juan J.; Díez-Pastor, José-Francisco; Arnaiz‐González, Álvar; Kuncheva, Ludmila I.

doi:10.1016/j.knosys.2019.105434

Cited by 31 publications

(5 citation statements)

References 69 publications

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“…In hybrid sampling methods, Random Balance [13] is a preprocessing strategy for binary imbalanced data, using random class ratios for random under-sampling and SMOTE oversampling. Based on this, Rodríguez et al [14] proposed the MultiRandBal method, extending it to multi-class imbalanced datasets. Unlike previous methods, this method uses randomly generated priors for sampling instead of class ratios.…”

Section: Multi-class Imbalance Data Classification Methodsmentioning

confidence: 99%

“…Recall is more concerned with the algorithm's ability to classify minority class samples, while Precision is more focused on majority class samples. To evaluate the average performance of the algorithm in a multiclass imbalanced environment, it is necessary to average the Recall and Precision values for each class to obtain macro-Recall and macro-Precision, as shown in Equations ( 11)- (14).…”

Section: Datasets and Evaluation Metricsmentioning

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%

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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“…Each batch of the drug were studied according to the pharmaco-technological and organoleptic indicators of the quality of powder masses and their solution. Scatter diagrams constructed based on the experimental results.Significant factors are taken from scatter diagrams, and their selection is proved by calculations [26,27]. This made it possible to determine the influence of investigated quantitative factors on quality indicators.…”

Section: Methodsmentioning

confidence: 99%

Development Of Formulation and Technology of Combined Generic Powder for Oral Solution in Sachets Based on the Quality by Design Approach

Panysheva¹

2022

GJMR

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Using quality by design approaches (dispersion analysis and random balance method), the formulation and technology of powder for oral solution in sachets were developed. It includes paracetamol, ascorbic acid, phenylephrine hydrochloride, and pheniramine maleate. The influence of 27 excipients from 5 functional groups on 11 quality attributes was established by the dispersion analysis. The preferred excipients were selected using the utility function. The quantities of preferable excipients (7 quantitative factors at three levels) were researched by the random balance method. Their influence on 11 pharmaco-technological parameters of the powder and the physical properties of the solution studied. The optimal composition of the powder for the oral solution established. 9 sequences of introducing components were analyzed by dispersion analysis. The optimal technology of the powder for oral solution in sachets byroll compaction is established.

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“…The method that provides good accuracy in handling class imbalance is Multiple Random Balance (MultiRandBal) [31], but with random sample determination, it is likely to be stuck with overlapping conditions. The overlapping conditions need to be well understood because there is a tendency to cause high accuracy in one class, which can reduce accuracy in other classes.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets

Hartono¹,

Ongko

2023

JOIV : Int. J. Inform. Visualization

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The multi-class imbalance problem has a higher level of complexity when compared to the binary class problem. The difficulty is due to the large number of classes that will present challenges related to overlapping between classes. Many approaches have been proposed to deal with these multi-class problems. One is a hybrid approach combining a data-level approach and an algorithm-level approach. This approach is done by the ensemble on the classifier and also oversampling on the minority class. SMOTE is an oversampling method that provides good performance, but this method is necessary to determine the best sample used in the interpolation process to generate new samples. The need for determining the best sample is related to the overlap between classes that always accompanies the multi-class imbalance problem. The existence of overlap requires efforts to determine the safe region to synthesize the sample in the oversampling process in SMOTE. The safe region is considered the best for synthesizing samples due to the lower tendency of overlapping. It can be done by constructing distance features to determine the safe region. The sample with the best distance and the lowest imbalance ratio will be selected as a sample in the over-sampling process with SMOTE. The main contribution of this research is the proposed method of Hybrid Approach with Distance Feature so that it can determine safe samples, with the main advantage being in addition to handling multi-class imbalances, it is also better for handling overlapping. The results of this study will be compared with Multiple Random Balance (MultiRandBal) which performs a random oversampling process. The results showed that the Augmented R-Value, Class Average Accuracy, Class Balance Accuracy, and Hamming Loss obtained in this method was better than the random oversampling process. These results also show that the Hybrid Approach with Distance Feature provides better results in handling multi-class imbalances when compared to MultiRandBal.

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Random Balance ensembles for multiclass imbalance learning

Cited by 31 publications

References 69 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

Development Of Formulation and Technology of Combined Generic Powder for Oral Solution in Sachets Based on the Quality by Design Approach

Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets

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