SMOTE-LOF for noise identification in imbalanced data classification

Asniar, Asniar; Maulidevi, Nur Ulfa; Surendro, Kridanto

doi:10.1016/j.jksuci.2021.01.014

Cited by 53 publications

(28 citation statements)

References 33 publications

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“…Therefore, the dataset is highly imbalanced, which usually leads to models with poor generalization ability. Both oversampling and undersampling methods have been widely utilized to solve the class imbalance challenge in different ML applications [76]- [78]. However, oversampling methods create balanced training sets by duplicating samples in the minority class, which could result in overfitting [79], while undersampling methods obtain balanced datasets by discarding selected majority class instances.…”

Section: Proposed Ccfd Methods a Data Resamplingmentioning

confidence: 99%

“…Both oversampling and undersampling methods have been widely utilized to solve the class imbalance challenge in different ML applications [76]- [78]. However, oversampling methods create balanced training sets by duplicating samples in the minority class, which could result in overfitting [79], while undersampling methods obtain balanced datasets by discarding selected majority class instances. Hence, undersampling could remove useful examples that might be crucial in building efficient ML models, and it is also inefficient in highly imbalanced datasets like the European credit card dataset.…”

Section: Proposed Ccfd Methods a Data Resamplingmentioning

confidence: 99%

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A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

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Section: Proposed Ccfd Methods a Data Resamplingmentioning

confidence: 99%

Section: Proposed Ccfd Methods a Data Resamplingmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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“…The algorithm that works on the first SMOTE differentiated the vectors of the features in the minority class and the nearest neighbor values from the minority class and then multiplied that value by a random number between 0 to 1. Next, the calculation results are added to the feature vector so that the vector value results are obtained from the new one [24].…”

Section: Smotementioning

confidence: 99%

Effects of Oversampling Smote and Spectral Transformations in the Classification of Mango Cultivars Using Near-Infrared Spectroscopy

Khumaidi¹,

Raafi'udin²

2022

International Journal on Advanced Science, Engineering and Information Technology

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Near-Infrared spectroscopy (NIR) is a non-destructive analytical technique that can provide chemical and structural information on samples in a speedy and accurate time. NIR has a wavelength of 750-2500 nm. However, the absorbance bands of the NIR spectrum are often broad, non-specific, and overlapping. NIR spectrum analysis requires a multivariate method which is very subjective to noise arising from instrumentation. There is no standard protocol in modeling for classification and prediction using NIR spectra. Several models have been developed with and without pre-processing techniques. The SMOTE technique can improve the model to predict all class responses accurately. This research contributes to creating a multiclass classification model for grouping mango cultivars by finding the best pre-processing technique and using SMOTE oversampling. The results of the four test scenarios on the model's performance built using the Support Vector Machine (SVM) that the best model is obtained using spectral transformations with LSNV and CLIP operations with 100% accuracy, precision, and recall values. The Decision Tree (DT) has the performance results in 100% model was obtained by using spectral transformation with LSNV, CLIP and SAVGOL operations with parameters {'deriv_order': 0,1, 2, 'filter_win': 11, 13, 'poly_order': 3}. Using SMOTE has better accuracy than without pre-processing, with an accuracy of 92% on SVM and 94% on DT. In comparison, the combination of SMOTE and Spectral Transformation gives classification results for SVM and DT with the same accuracy of 96%, better than using SMOTE only.

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“…The Synthetic Minority Oversampling Technique (SMOTE) is the established geometric approach to balance classes by oversampling the minority class [10]. Multiple variations of SMOTE have been developed [11], including novel approaches such as the SMOTE-LOF which takes into account the Local Outlier Factor [12] to identify noisy synthetic samples. Furthermore, overlap samples from different classes has been reported as a big issue in imbalance problems.…”

Section: Related Workmentioning

confidence: 99%

A Topological Data Analysis Based Classifier

Kindelan¹,

Frías²,

Cerda³

et al. 2021

Preprint

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Topological Data Analysis (TDA) is an emergent field that aims to discover topological information hidden in a dataset. TDA tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes an algorithm that applies TDA directly to multi-class classification problems, without any further ML stage, showing advantages for imbalanced datasets. The proposed algorithm builds a filtered simplicial complex on the dataset. Persistent Homology (PH) is applied to guide the selection of a sub-complex where unlabeled points obtain the label with the majority of votes from labeled neighboring points. We select 8 datasets with different dimensions, degrees of class overlap and imbalanced samples per class. On average, the proposed TDABC method was better than KNN and weighted-KNN. It behaves competitively with Local SVM and Random Forest baseline classifiers in balanced datasets, and it outperforms all baseline methods classifying entangled and minority classes. IntroductionClassification is a Machine Learning (ML) task that employs known data labels to label data with an unknown category. Classification faces challenges such as high dimensionality, noise, and imbalanced data distributions. TDA has been 1 This paper is under consideration at Pattern Recognition Letters Journal.

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SMOTE-LOF for noise identification in imbalanced data classification

Cited by 53 publications

References 33 publications

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Effects of Oversampling Smote and Spectral Transformations in the Classification of Mango Cultivars Using Near-Infrared Spectroscopy

A Topological Data Analysis Based Classifier

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