SMOTE-DGC: An Imbalanced Learning Approach of Data Gravitation Based Classification

Peng, Lizhi; Zhang, Haibo; Yang, Bo; Chen, Yuehui; Zhou, Xiaoqing

doi:10.1007/978-3-319-42294-7_11

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Hence this method qualifies as a hybrid strategy since it oversamples and then cleans the synthetically generated minority data by undersampling. SMOTE was integrated with data gravitation‐based classification (DGC) in Reference 65, a physical‐inspired classification model that fails under conditions of class imbalance; the hybrid was named SMOTE‐DGC. The hybrid method SSO Maj ‐SMOTE‐SSO Min 66 involves careful selection of the population of both classes, and only representative samples that carry discriminatory information are retained on both sides.…”

Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Susan

Kumar

2020

Engineering Reports

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This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real-world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to insufficient training samples from the minority class. The current machine learning and deep learning algorithms are trained on datasets that are insufficiently represented in certain categories. On the other hand, some other classes have surplus samples due to the ready availability of data from these categories.Conventional solutions suggest undersampling of the majority class and/or oversampling of the minority class for balancing the class distribution prior to the learning phase. Though this problem of uneven class distribution is, by and large, ignored by researchers focusing on the learning technology, a need has now arisen for incorporating balance correction and data pruning procedures within the learning process itself. This paper surveys a plethora of conventional and recent techniques that address this issue through intelligent representations of samples from the majority and minority classes, that are given as input to the learning module. The application of nature-inspired evolutionary algorithms to intelligent sampling is examined, and so are hybrid sampling strategies that select and retain the difficult-to-learn samples and discard the easy-to-learn samples. The findings by various researchers are summarized to a logical end, and various possibilities and challenges for future directions in research are outlined. K E Y W O R D Sclass-imbalance problem, hybrid sampling, imbalanced data, oversampling, sampling, undersampling INTRODUCTIONLearning from imbalanced datasets results in a bias toward the majority class whose labeled samples are available in plenty as compared to the insufficiently represented minority class. 1 In data mining, factors that bring down the classifier performance are the intrinsic characteristics of the data and an uneven class distribution. 2 Lack of adequate data in the minority class results in a fuzzy and ever-varying decision boundary, leading to erroneous results. The problem isThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Susan

Kumar

2020

Engineering Reports

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“…Qiong et al 39 proposed genetic algorithm-based synthetic minority oversampling technique (GASMOTE) to improve SMOTE-based on the genetic algorithm (GA) by setting different sampling rates for different minority samples. Moreover, SMOTE has been integrated with numerous machine learning 9,[40][41][42] and deep learning algorithms. 43 Undersampling methods also have many different forms including random undersampling, 34 inverse random undersampling, 44 and the EasyEnsemble and BalanceCascade undersampling strategies.…”

Section: Methods For Imbalanced Learningmentioning

confidence: 99%

Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets

Zheng

et al. 2017

International Journal of Distributed Sensor Networks

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In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy-synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy-synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline-synthetic minority oversampling technique methods, fuzzy-synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets.

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“…Two methods of data sampling were proposed as well. The first one is Under-Sampling Imbalanced Data Gravitation Classification (UI-DGC) [6] and the second one is Synthetic Minority Oversampling Technique Data Gravitation Classification (SMOTE-DGC) [7]. The imbalanced data sets may be met in the above-mentioned problem of the Internet traffic dangers identification [4], in the prediction of blood donation [8,9], in occupancy indoor detection [10], and in many other fields.…”

Section: Introductionmentioning

confidence: 99%

Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

Rybak¹,

Dudczyk²

2021

Applied Sciences

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The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their geometrical divide. In the previous analyses it was established that the Geometrical Divide (GD) method outperforms the algorithm creating the data particles based on classes by a compound of 1 ÷ 1 cardinality. This occurs in the process of balanced data sets classification, in which class centroids are close to each other and the groups of objects, described by different labels, overlap. The purpose of the article was to examine the efficiency of the Geometrical Divide method in the unbalanced data sets classification, by the example of real case-occupancy detecting. In addition, in the paper, the concept of the Unequal Geometrical Divide (UGD) was developed. The evaluation of approaches was conducted on 26 unbalanced data sets-16 with the features of Moons and Circles data sets and 10 created based on real occupancy data set. In the experiment, the GD method and its unbalanced variant (UGD) as well as the 1CT1P approach, were compared. Each method was combined with three data particle mass determination algorithms-n-Mass Model (n-MM), Stochastic Learning Algorithm (SLA) and Bath-update Algorithm (BLA). k-fold cross validation method, precision, recall, F-measure, and number of used data particles were applied in the evaluation process. Obtained results showed that the methods based on geometrical divide outperform the 1CT1P approach in the imbalanced data sets classification. The article’s conclusion describes the observations and indicates the potential directions of further research and development of methods, which concern creating the data particle through its geometrical divide.

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SMOTE-DGC: An Imbalanced Learning Approach of Data Gravitation Based Classification

Cited by 5 publications

References 26 publications

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets

Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

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