Adaptive learning of minority class prior to minority oversampling

Sadhukhan, Payel; Palit, Sarbani

doi:10.1016/j.patrec.2020.05.020

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Regardless of whether it's the Borderline-SMOTE, Safe-level SMOTE, or ADASYN method, the absence of a specified sampling region for each minority class instance can lead to the generation of erroneous or noisy instances. Budapest et al proposed Learning Minority Class prior to Minority Oversampling (LMCMO) [28] and Adaptive Learning of Minority Class prior to Minority Oversampling (ALMCMO) [29] estimate the minority class spaces before generating synthetic minority points to avoid producing incorrect instances. SMOTE-ENN combines the SMOTE oversampling technique with the Edited Nearest Neighbors (ENN) undersampling technique [30].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Safe-Region Diversity Oversampling Algorithm for Imbalanced Classification

Tao,

Li,

Wang

et al. 2024

IEEE Access

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The challenge of imbalanced data classification stems from the uneven distribution of data across classes, which is a formidable obstacle for traditional classifiers. Although numerous methods have been proposed to address this problem, it is widely recognized that the artificial generation of instances through oversampling methods is a more effective and versatile strategy for balancing the class distribution. We identify that existing oversampling methods are susceptible to generating unnecessary and noisy instances in complex imbalanced scenarios. In light of this, a novel approach called Adaptive Safe-Region Diversity Oversampling (ASRDO) is introduced to tackle difficulties in imbalanced learning. ASRDO starts by calculating the distance from each minority class instance to its nearest majority class instances. Utilizing this distance as a radius, it defines a safe hyperspherical sampling region for each minority instance. The algorithm then assigns weights to minority instances based on the density within their respective sampling regions and the average distance to k nearest majority instances. Finally, It randomly selects two instances from the k nearest minority instances, generates a new direction vector by linear combination, and synthesizes minority instances along this direction vector within the sampling region. Experimental results show a significant performance improvement of the proposed method compared to prevalent oversampling methods on 32 public datasets. A Python implementation of ASRDO is provided for reference.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Safe-Region Diversity Oversampling Algorithm for Imbalanced Classification

Tao,

Li,

Wang

et al. 2024

IEEE Access

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“…The classification accuracy results in the majority class will be better when compared to the minority class [4]. In datasets that experience class imbalance, there is a tendency that the minority class is a class with information that tends to be more interesting than the majority class [5]. Class imbalance handling can increase overall classification accuracy [6].…”

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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“…Algorithm modification is to improve the existing algorithm or classification paradigm to adapt to the learning of minority class. There are three classic techniques for data preprocessing, namely under-sampling [12], oversampling [10], [13], [14], [15] and mixed sampling, which are also common methods. Under-sampling is a reasonable censorship of the majority class, while over-sampling is an effective supplement to the minority class data.…”

Section: Introductionmentioning

confidence: 99%