Class imbalance learning using UnderBagging based kernelized extreme learning machine

Raghuwanshi, Bhagat Singh; Shukla, Sanyam

doi:10.1016/j.neucom.2018.10.056

Cited by 70 publications

(29 citation statements)

References 33 publications

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“…The experiments are based on 44 datasets collected from the KEEL dataset repository [1]. They have been widely used for class imbalance learning in related literatures (Bellinger et al, 2020;Galar et al, 2012;Guo et al, 2020;Gupta et al, 2019;Raghuwanshi and Shukla, 2019;Sunder and Punniyamoorthy, 2019;Tsai et al, 2019). The basic information of the 44 datasets is shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…To achieve these two research objectives, the experiments are based on 44 different domain class imbalanced datasets from the KEEL dataset repository (Galar et al, 2012), which have been widely used in related literatures for class imbalance learning (Bellinger et al, 2020;Galar et al, 2012;Guo et al, 2020;Gupta et al, 2019;Raghuwanshi and Shukla, 2019;Sunder and Punniyamoorthy, 2019;Tsai et al, 2019). In addition, three algorithms including DROP3, the GA and IB3, which have been considered representative baseline instance selection algorithms, are used individually and their respective performances are compared (Garcia et al, 2012;Huang et al, 2018;Lin et al, 2015;Tsai et al, 2019).…”

Section: Class Imbalanced Datasets 771mentioning

confidence: 99%

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Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Wang

Tsai

Lin

2021

DTA

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PurposeClass imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers.Design/methodology/approachIn this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values.FindingsThe experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values.Originality/valueThe novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.

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Section: Methodsmentioning

confidence: 99%

Section: Class Imbalanced Datasets 771mentioning

confidence: 99%

Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Wang

Tsai

Lin

2021

DTA

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“…A weighted online sequential learning machine with kernels is used instead of using random feature mapping to handle multiclass imbalanced problem. Recently, a hybrid approach that combines both data-level and algorithm-level for the imbalance problem has been proposed [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Facial Action Units for Training Convolutional Neural Networks

Pham

Won

2019

IEEE Access

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This paper deals with the problem of training convolutional neural networks (CNNs) with facial action units (AUs). In particular, we focus on the imbalance problem of the training datasets for facial emotion classification. Since training a CNN with an imbalanced dataset tends to yield a learning bias toward the major classes and eventually leads to deterioration in the classification accuracy, it is required to increase the number of training images for the minority classes to have evenly distributed training images over all classes. However, it is difficult to find the images with a similar facial emotion for the oversampling. In this paper, we propose to use the AU features to retrieve an image with a similar emotion. The query selection from the minority class and the AU-based retrieval processes repeat until the numbers of training data over all classes are balanced. Also, to improve the classification accuracy, the AU features are fused with the CNN features to train a support vector machine (SVM) for final classification. The experiments have been conducted on three imbalanced facial image datasets, RAF-DB, FER2013, and ExpW. The results demonstrate that the CNNs trained with the AU features improve the classification accuracy by 3%-4%.

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“…There are two approaches to solving this problem in dealing with class imbalance: the data level approach, the algorithmic approach, and hybrid-based approaches [8], [9]. The data-level approach can use the sampling method.…”

Section: Introductionmentioning

confidence: 99%

Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data

Kurniawati

Prabowo

2022

IJ-AI

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<span lang="EN-US">Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in this study is the synthetic minority oversampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) algorithms. The algorithm approach used in this study is the ensemble classifier using AdaBoost and bagging with the classification and regression tree (CART) as learner-based. The best model obtained from the experimental results in accuracy, precision, recall, and f-measure using ADASYN-N and AdaBoost-CART.</span>

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Class imbalance learning using UnderBagging based kernelized extreme learning machine

Cited by 70 publications

References 33 publications

Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Facial Action Units for Training Convolutional Neural Networks

Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data

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