DBMUTE: density-based majority under-sampling technique

Bunkhumpornpat, Chumphol; Sinapiromsaran, Krung

doi:10.1007/s10115-016-0957-5

Cited by 55 publications

(20 citation statements)

References 19 publications

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“…As a result, a more balanced dataset is obtained. Bunkhumpornpat et al [35] proposed a majority class undersampling technique based on density-based spatial clustering algorithm (DBMUTE). DBMUTE was designed to eliminate negative instances from the overlapping region.…”

Section: Related Workmentioning

confidence: 99%

“…The second evaluation metric we used is geometric mean (Gmean), which measures the balance between the TPR and the true negative rate (TNR) and is defined as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi TPR Â TNR p . Finally, we used F 1 Score between the TPR and the FPR [35] and is defined as F 1 Score = b Â TPRÂFPR TPRþFPR , with b value = 2. The experiments were implemented using Python 3.6 and were carried out on a Windows 10 machine with 16 GB RAM and a 2.7 GHz processor.…”

Section: Settings and Implementation Detailsmentioning

confidence: 99%

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CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

Elyan

Moreno‐García

Jayne

2020

Neural Comput & Applic

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Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets requires employing some methods to compact the problem. In this paper, we propose a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minority class instances using an oversampling method. Unlike other undersampling methods, which suffer data loss, our method preserves the majority class instances, yet significantly reduces its dominance, resulting in a more balanced dataset and hence improving the results. A large-scale experiment using 60 public datasets was carried out to validate the proposed methods. The results across three standard evaluation metrics show the comparable and superior results with other common and state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Settings and Implementation Detailsmentioning

confidence: 99%

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

Elyan

Moreno‐García

Jayne

2020

Neural Comput & Applic

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“…Zhu et al [70] implemented ENN undersampling method and adaptive synthetic oversampling approach to solve the class imbalance problem and they also used the two-step FS technique to optimize the feature set. The technique mentioned by Bunkhumpornpat and Sinapiromsaran [11], used the densitybased majority undersampling technique (DBMUTE) that has the ability to adapt directly density reachable graph. They showed improved results on UCI health monitoring datasets: Haberman's survival and diabetes.…”

Section: Literature Surveymentioning

confidence: 99%

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw

Ahmed

Malakar³

et al. 2021

Complex Intell. Syst.

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Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

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“…The main difference between our proposal and both these methods is that, unlike DBMIST-US, these are for over-sampling. On the other hand, the DBMUTE technique combines DBSCAN with the under-sampling MUTE algorithm [56] to discover and remove majority class instances from the overlapping region [57], but this is not intended to identify and remove noisy instances.…”

Section: Differences Between Dbmist-us and Related Workmentioning

confidence: 99%

A New Under-Sampling Method to Face Class Overlap and Imbalance

et al. 2020

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Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases.

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DBMUTE: density-based majority under-sampling technique

Cited by 55 publications

References 19 publications

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

A New Under-Sampling Method to Face Class Overlap and Imbalance

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