Improving interpolation-based oversampling for imbalanced data learning

Zhu, Tuanfei; Lin, Yaping; Liu, Yonghe

doi:10.1016/j.knosys.2019.06.034

Cited by 56 publications

(19 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other ways of oversampling include, but are not limited to, the work of [91,92,93,94,78,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119] The validation process is what all oversampling methods have in common, which is basically the evaluation of the classifier's performance employed to classify the oversampled datasets using one or more accuracy measures such as Accuracy, Precision, Recall, F-measure, G-mean, Specificity, Kappa, Matthews correlation coefficient (MCC), Area under the ROC Curve (AUC), True positive rate, False negative (FN), False positive (FP), True positive (TP), True negative (TN), and ROC curve. Table 1 lists 72 oversampling methods, including their known names, references, the number of datasets utilized, the number of classes in these datasets, the classifiers employed, and the performance metrics used to validate the classification results after oversampling.…”

Section: Literature Review Of Oversampling Methodsmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat

Tarawneh

Altarawneh

2022

Preprint

View full text Add to dashboard Cite

For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

show abstract

Section: Literature Review Of Oversampling Methodsmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat

Tarawneh

Altarawneh

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This means that a better model can be obtained by creating a model by expanding the experimental data than the modeling result using 18 data elements, which is the actual data. In fact, existing studies have reported that the creation of virtual data improves the R 2 and MSE values of the model [47][48][49].…”

Section: Evaluation Of the Modelmentioning

confidence: 99%

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Wie

Lee

et al. 2020

Materials

View full text Add to dashboard Cite

The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.

show abstract

“…Many extensions and modifications to the SMOTE algorithm have been proposed, e.g., the Borderline SMOTE [28] (bSMOTE), which detects and uses the borderline observations to generate new synthetic samples. Other over-sampling concepts employ clusterization [14], genetic algorithms [29], potential functions [30], neighborhood analysis [30], interpolation [31], and identification of harder to learn observations [32].…”

Section: Related Workmentioning

confidence: 99%

An “Outside the Box” Solution for Imbalanced Data Classification

Jegierski

Saganowski

2020

IEEE Access

View full text Add to dashboard Cite

A common problem of the real-world data sets is the class imbalance, which can significantly affect the classification abilities of classifiers. Numerous methods have been proposed to cope with this problem; however, even state-of-the-art methods offer a limited improvement (if any) for data sets with critically under-represented minority classes. For such problematic cases, an "outside the box" solution is required. Therefore, we propose a novel technique, called enrichment, which uses the information (observations) from the external data set(s). We present three approaches to implement the enrichment technique: (1) selecting observations randomly, (2) iteratively choosing observations that improve the classification result, (3) adding observations that help the classifier to determine the border between classes better. We then thoroughly analyze developed solutions on ten real-world data sets to experimentally validate their usefulness. On average, our best approach improves the classification quality by 27%, and in the best case, by outstanding 66%. We also compare our technique with the state-of-the-art methods. We find that our technique surpasses the existing methods performing, on average, 21% better. The advantage is especially noticeable for the smallest data sets, for which existing methods failed, while our solutions achieved the best results. Additionally, the enrichment technique applies to both the multi-class and binary classification tasks. It can also be combined with other techniques dealing with the class imbalance problem.

show abstract

Improving interpolation-based oversampling for imbalanced data learning

Cited by 56 publications

References 44 publications

Stop Oversampling for Class Imbalance Learning: A Critical Review

Stop Oversampling for Class Imbalance Learning: A Critical Review

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

An “Outside the Box” Solution for Imbalanced Data Classification

Contact Info

Product

Resources

About