The generation mechanism of synthetic minority class examples

Tang, Sheng; Chen, Siping

doi:10.1109/itab.2008.4570642

Cited by 39 publications

(12 citation statements)

References 8 publications

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“…Synthetic Minority Over-sampling TEchnique SMOTE [53] ADAptive SYNthetic Sampling ADASYN [54] Borderline-Synthetic Minority Over-sampling TEchnique SMOTE-BL [55] Safe Level Synthetic Minority Over-sampling TEchnique SMOTE-SL [56] Random Oversampling ROS [57] Adjusting the Direction Of the synthetic Minority clasS examples ADOMS [58] In this section, we analyzed the datasets to be used, as well as some of the most representative associative classifiers. In addition, we mentioned the sampling algorithms for class balancing we will use.…”

Section: Name Acronym Referencementioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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Section: Name Acronym Referencementioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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“…ADOMS: Adjusting the Direction Of the synthetic Minority clasS examples [30], This method works similar to SMOTE. However, this method generates synthetic instances along the first principal component axis (PCA) of the local data of the distribution using the nearest k neighbors.…”

Section: Algorithms To Comparementioning

confidence: 99%

Credit Assignment: Using Resampling Methods for Dealing with the Class Imbalance Problem

Cruz-Galarza¹,

Villuendas-Rey²,

Yáñez-Márquez³

2018

RCS

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Nowadays, credit assignment constitutes a way in which persons or entities access to money. However, bad clients can cause big distress to financial institutions. If there are appropriate data banks whose patterns contain financial information from the scope of the allocation of credits, the intelligent pattern classifiers are ideal candidates to solve the credit assignment problem. Nevertheless, working with data sets from credit environment has the disadvantage that, in most of the cases, have unbalanced classes. This situation represents a problem at the moment of work with this kind of datasets due to the fact that unbalanced classes, in general, create biased learning. The consequences of this are reflected during the testing phase because the biased learning causes the classifiers to just recognize appropriately the elements of the ruling class and therefore, give us inaccuracy results. In this paper, we tested some undersampling and oversampling algorithms, and we compared their performance, based on the Imbalance Ratio measure, over different well-known credit related datasets.

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“…ADOMS The Adjusting the Direction Of the synthetic Minority clasS method, setting the direction of the synthetic minority class samples, this works like SMOTE, but it generates synthetic examples along the first component of the main axis of the local data distribution [45].…”

Section: Over-sampling Approachesmentioning

confidence: 99%

A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem

Alejo¹,

Monroy-de-Jesús

Pacheco-Sánchez

et al. 2016

Applied Sciences

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Abstract:In this work, we developed a Selective Dynamic Sampling Approach (SDSA) to deal with the class imbalance problem. It is based on the idea of using only the most appropriate samples during the neural network training stage. The "average samples"are the best to train the neural network, they are neither hard, nor easy to learn, and they could improve the classifier performance. The experimental results show that the proposed method is a successful method to deal with the two-class imbalance problem. It is very competitive with respect to well-known over-sampling approaches and dynamic sampling approaches, even often outperforming the under-sampling and standard back-propagation methods. SDSA is a very simple method for automatically selecting the most appropriate samples (average samples) during the training of the back-propagation, and it is very efficient. In the training stage, SDSA uses significantly fewer samples than the popular over-sampling approaches and even than the standard back-propagation trained with the original dataset.

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The generation mechanism of synthetic minority class examples

Cited by 39 publications

References 8 publications

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Credit Assignment: Using Resampling Methods for Dealing with the Class Imbalance Problem

A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem

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