CLUS: A new hybrid sampling classification for imbalanced data

Prachuabsupakij, Wanthanee

doi:10.1109/jcsse.2015.7219810

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…Second, Under-sampling tries to balance the data by subtracting from the majority class. Since these categories have their own strengths and weaknesses, a third category called hybrids was created [27].…”

Section: Sampling and Synthetic Data Generationmentioning

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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Section: Sampling and Synthetic Data Generationmentioning

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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“…Existing approaches processing imbalanced data can be generally divided into two categories [1,2]. The first category is based on resampling at the data level, which either (i) increases the number of samples using upsampling by synthesizing new data or copying the original data, or (ii) reduces the number of samples using subsampling by extracting a small amount of data.…”

Section: Introductionmentioning

confidence: 99%

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

Zhou

Zhai

et al. 2019

Sensors

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The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances.

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