An Imbalance Compensation Framework for Background Subtraction

Zhang, Xiang; Zhu, Ce; Wu, Honggang; Li, Zhi; Xu, Yuanyuan

doi:10.1109/tmm.2017.2701645

Cited by 34 publications

(5 citation statements)

References 48 publications

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“…In Paper [22], a mixed re-sampling method has been proposed using SMOTE and an under-Sampling algorithm to solve the noise problem. Another hybrid re-sampling approach combines spatiotemporal over-sampling and selective un-der-sampling to align foreground and background pixels in a video [23]. Two separate and parallel particle swarm optimization processes used a mixed re-sampling method [24].…”

Section: Sampling Methodsmentioning

confidence: 99%

A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

et al. 2021

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Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC.

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Section: Sampling Methodsmentioning

confidence: 99%

A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

et al. 2021

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“…Bach et al Also presented a hybrid algorithm in osteoporosis images that improved classification performance by combining random first and sampling data [36]. Combining different classification algorithms with the simultaneity of overclocking and under-sampling in special cases has improved the performance of the classifiers used [45], [46], [47].…”

Section: Sampling and Synthetic Data Generationmentioning

confidence: 99%

“…Active Learning is used to learn unlabeled data. Also, Active Learning techniques are conventionally used to solve problems related to Semi-supervised Learning [46]. Some scientific papers have used this method to work with imbalanced data.…”

Section: Active Learningmentioning

confidence: 99%

Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data

Alimoradi,

Daliri,

Zabihimayvan

et al. 2024

Preprint

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Proper grouping in classifier algorithms is a critical element of supervised learning. The first step in this is to have the correct data. Data that has a problem is worse than not having it. One of the biggest problems inherent in natural data is its imbalance. For a classifier algorithm to achieve its best performance, the first step is to fix the problem of data imbalance. To work with real datasets, the first step is to balance the data. The main problem with existing algorithms is to duplicate minority data and generate data that make outlines part of the primary data. The Statistic Deviation Mode Balancer (SDMB) algorithm solves this problem by making samples that adhere to the original data structure. Our proposed algorithm generates data that is very similar to the original data with the help of standard deviation and the amount of minor data mode and moving away from the majority part. Using these two parameters, the SDMB algorithm avoids Outlier data and generates clean data. The output of this algorithm is a balance datasheet that helps classifier algorithms learn the best way from the data. Different classifier algorithms with entirely different methods have been tested to prove this point. First, we balanced the different datasets with our method. Then, with varying classifier algorithms, we compared it with other existing algorithms. This experiment showed that our proposed algorithm is superior to other competitors and can be used in the work process of real datasets.

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“…e technique of balancing the class distribution for a classification dataset with a skewed class distribution is known as undersampling [28,29]. To balance the class distribution, undersampling removes the training dataset examples which pertain to the majority class, such as reducing the skew from a 1 : 100 to a 1 : 10, 1 : 2, or even a 1 : 1 class distribution.…”

Section: Near Miss-based Undersamplingmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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An Imbalance Compensation Framework for Background Subtraction

Cited by 34 publications

References 48 publications

A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

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