Kernel-based SMOTE for SVM classification of imbalanced datasets

Mathew, Josey; Luo, Ming; Pang, Chee Khiang; Chan, Hian Leng

doi:10.1109/iecon.2015.7392251

Cited by 57 publications

(25 citation statements)

References 14 publications

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“…Undersampling majority classes often leads to the exclusion of important instances required to differentiate between two classes. This has led researchers to develop more complex methods referred to as synthetic minority oversampling techniques (SMOTE) [18,19]. This approach can reduce the risk of data loss and overfitting; however, it is still prone to over-generalization or variance [20,21].…”

Section: Data-based Methodsmentioning

confidence: 99%

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Lin

Nguyen

2020

Applied Sciences

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Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).

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Section: Data-based Methodsmentioning

confidence: 99%

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Lin

Nguyen

2020

Applied Sciences

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“…Majumder et al (2016) proposed a novel method, SMOTE and ADAptive SYNthetic Sampling (ADASYN) for balancing the highly imbalanced geometric feature set (extracted from the shoulder pain database). Mathew et al (2015) proposed a Kernel based SMOTE (KSMOTE) method/ technique that creates synthetically minority information in the element space of SVM classifier for class imbalance problem. Pelayo and Dick (2007) explore the use of stratificationbased re-sampling approach in software defect prediction.…”

Section: Data Level Methodsmentioning

confidence: 99%

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Rekha¹,

Tyagi²,

Reddy³

2019

Journal of Computer Science

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In today's world, most of the data (real world) is present in imbalanced form by nature. This is because of not having efficient algorithms to put this data (i.e., generated data by billion of internetconnected devices (IoTs)) in respective format. Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms. The imbalanced dataset consists of a majority class and a minority class, where the majority class takes the lead over the minority class. Generally, several standard learning algorithms assume the balanced class distribution or equal misclassification costs. If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance. To overcome this problem (or improving accuracy of deision/prediction-making process), data mining and machine learning researchers have addressed the problem of imbalanced data using datalevel, algorithmic level and ensemble or hybrid methods. This article presents a systematic literature review and analyze the results of more than 400 research papers published between 2002-2017 (till June 2017), resulting in a broader and elaborate investigation of the literature in this area of research. Note that extension of this article/work will contain till December 2018 research articles, which will be published in June 2019 (now these more papers/articles did not include due to no. of pages/space issues). The systematic analysis of the research literature has focus on the key role of Data Intrinsic Problems in classification, handling the imbalanced data and the techniques used to overcome the skewed distribution. Furthermore, this article reveals patterns, trends and gaps in the existing literature and discusses briefly the next generation research directions in this area.

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“…Specifically. The synthetic minority oversampling technique (SMOTE) has proven to be quite powerful which has achieved a great deal of success in various applications [7], [8]. SMOTE creates artificial data based on the similarities between existing minority samples.…”

Section: Asampling Techiniquementioning

confidence: 99%

Training deep neural networks on imbalanced data sets

Wang

Liu

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

362

230

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Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.

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Kernel-based SMOTE for SVM classification of imbalanced datasets

Cited by 57 publications

References 14 publications

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Training deep neural networks on imbalanced data sets

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