UFFDFR: Undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection for imbalanced data classification

Zheng, Ming; Li, Tong; Zheng, Xiaoyao; Yu, Qingying; Chen, Chuanming; Zhou, Ding; Lv, Changlong; Yang, Wei

doi:10.1016/j.ins.2021.07.053

Cited by 38 publications

(13 citation statements)

References 40 publications

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“…This resulted in two classes (minority and majority), which is a clear indication of an unbalanced dataset. − In such cases, the cost of classifying and assigning all samples into the majority class (i.e., composite class in this case) is relatively low. In such circumstances, oversampling methods are handy tools to overcome imbalanced data by generating an adequate amount of minority class samples . In this study, three distinct oversampling methods were used, and they are explained here.…”

Section: Methodsmentioning

confidence: 99%

“…In such circumstances, oversampling methods are handy tools to overcome imbalanced data by generating an adequate amount of minority class samples. 31 In this study, three distinct oversampling methods were used, and they are explained here. In what follows, x i is a vector repressing the extracted features, namely, T i 0 , T i L , m i , and dt i , sampled during test i:…”

Section: Sample Preparationmentioning

confidence: 99%

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A Machine Learning Approach for Polymer Classification Based on the Thermal Response under Data Scarcity─Tested on PMMA

Vaghefi,

Barforoushan,

Nejabat

et al. 2023

Ind. Eng. Chem. Res.

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An important application of machine learning techniques is the intelligent nondestructive testing of polymers. However, data scarcity and class imbalance (for real applications) shape some of the involved challenges. In order to tackle these challenges, an intelligent screening framework for poly(methyl methacrylate) (PMMA) samples is studied here. An efficient thermal and experimental test is designed and coupled with several machine learning tools for quick classification with four features. Furthermore, a set of sampling techniques are employed/compared; these techniques are Random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling (ADASYN). Then, Linear Discriminant Analysis, K-Nearest Neighbor, Naive Bayes, decision tree, random forest, pattern recognition network, support vector machine, and ensemble learning are employed for classification. For assessing these algorithms, their performances are evaluated using a collection of metrics (i.e., Geometric-mean, F1 score, Matthews correlation coefficient, accuracy, true positive rate, true negative rate, positive predictive value, and negative predictive value). Among others, the average G-mean measures, which is a paramount measure for assessing the imbalance data, are increased from 75.97% (original data) to 94.24% (ROS), 93.49% (SMOTE) and 91.27% (ADASYN). That is a clear proof of successful oversampling. The final results show that ROS oversampling coupled with Ensemble classification methods can significantly improve all performance metrics for PMMA classification.

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Section: Methodsmentioning

confidence: 99%

Section: Sample Preparationmentioning

confidence: 99%

A Machine Learning Approach for Polymer Classification Based on the Thermal Response under Data Scarcity─Tested on PMMA

Vaghefi,

Barforoushan,

Nejabat

et al. 2023

Ind. Eng. Chem. Res.

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“…New sampling proposals for specific use cases like e.g. balancing imbalanced classes [49,57], online sampling [7,42] or reinforcement learning [13,15] emerge constantly. We do not intend to cover all of these here, but simply mention that there is a large and growing body of literature to assist in obtaining the best possible sampling strategy for a given purpose.…”

Section: Statistical Methodologymentioning

confidence: 99%

Data Representativity for Machine Learning and AI Systems

Clemmensen¹,

Kjærsgaard²

2022

Preprint

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Data representativity is crucial when drawing inference from data through machine learning models. Scholars have increased focus on unraveling the bias and fairness in the models, also in relation to inherent biases in the input data. However, limited work exists on the representativity of samples (datasets) for appropriate inference in AI systems. This paper analyzes data representativity in scientific literature related to AI and sampling, and gives a brief overview of statistical sampling methodology from disciplines like sampling of physical materials, experimental design, survey analysis, and observational studies. Different notions of a 'representative sample' exist in past and present literature. In particular, the contrast between the notion of a representative sample in the sense of coverage of the input space, versus a representative sample as a miniature of the target population is of relevance when building AI systems. Using empirical demonstrations on US Census data, we demonstrate that the first is useful for providing equality and demographic parity, and is more robust to distribution shifts, whereas the latter notion is useful in situations where the purpose is to make historical inference or draw inference about the underlying population in general, or make better predictions for the majority in the underlying population. We propose a framework of questions for creating and documenting data, with data representativity in mind, as an addition to existing datasheets for datasets. Finally, we will also like to call for caution of implicit, in addition to explicit, use of a notion of data representativeness without specific clarification.

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“…However, when dealing with a large data sets, the computation cost is quite severe [27]. UFFDFR [28] uses a denoising stage to reduce the effect of noise samples and fuzzy c-means clustering algorithm to improve the performance of classification, and selects the representative samples based on distance. The three parts of UFFDFR are in sequence, so any unsatisfactory part will affect the final result.…”

Section: Related Workmentioning

confidence: 99%

A Method for Class-Imbalance Learning in Android Malware Detection

Guan

Mao

2021

Electronics

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More and more Android application developers are adopting many different methods against reverse engineering, such as adding a shell, resulting in certain features that cannot be obtained through decompilation, which causes a serious sample imbalance in Android malware detection based on machine learning. Hence, the researchers have focused on how to solve class-imbalance to improve the performance of Android malware detection. However, the disadvantages of the existing class-imbalance learning are mainly the loss of valuable samples and the computational cost. In this paper, we propose a method of Class-Imbalance Learning (CIL), which first selects representative features, uses the clustering K-Means algorithm and under-sampling to retain the important samples of the majority class while reducing the number of samples of the majority class. After that, we use the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to generate minority class samples for data balance, and finally use the Random Forest (RF) algorithm to build a malware detection model. The result of experiments indicates that CIL effectively improves the performance of Android malware detection based on machine learning, especially for class imbalance. Compared with existing class-imbalance learning methods, CIL is also effective for the Machine Learning Repository from the University of California, Irvine (UCI) and has better performance in some data sets.

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UFFDFR: Undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection for imbalanced data classification

Cited by 38 publications

References 40 publications

A Machine Learning Approach for Polymer Classification Based on the Thermal Response under Data Scarcity─Tested on PMMA

A Machine Learning Approach for Polymer Classification Based on the Thermal Response under Data Scarcity─Tested on PMMA

Data Representativity for Machine Learning and AI Systems

A Method for Class-Imbalance Learning in Android Malware Detection

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