An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification

Zhang, Yong; Zuo, Tingting; Fang, Lichao; Li, Jun; Xing, Zongyi

doi:10.1109/access.2020.3047741

Cited by 33 publications

(41 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In over-sampling methods, new samples are created based on samples from the minority class to reach a more balanced class distribution of samples while strengthening class boundaries [40,41]. However, over-sampling may lead to overfitting because it duplicates o r s y nt hesis es a minority of samples [42]. As the number of samples increases, the training time also increases [43].…”

Section: ) Data-level Methodsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

View full text Add to dashboard Cite

Medical datasets are usually imbalanced, where negative cases severely outnumber p osit iv e cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO an d NLST, wit h imb alan ce ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance o f 23 clas s imb alan ce methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, Etc.), seven over-sampling methods (SMOTE, Etc.), an d t wo integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, Etc.). The results show that class imbalance learning can improve the classification abilit y o f t h e mo d el. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable met h od, an d the AUC in the model is generally higher than in other ways. Using ROS, the random forest p erforms t h e best predictive ability and is more suitable for the lung cancer datasets used in this study.

show abstract

Section: ) Data-level Methodsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Solutions proposed in previous literature can be generally divided into three categories (Krawczyk, 2016): (1) Data-level methods that employ undersampling or over-sampling technique to balance the class distributions (Barua et al, 2014;Smith et al, 2014;Sobhani et al, 2014;Zheng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

SetConv: A New Approach for Learning from Imbalanced Data

Gao¹,

Li²,

Yang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

View full text Add to dashboard Cite

For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high. To address this problem, we propose a set convolution (SetConv) operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution. We prove that our proposed algorithm is permutation-invariant despite the order of inputs, and experiments on multiple large-scale benchmark text datasets show the superiority of our proposed framework when compared to other SOTA methods.

show abstract

“…Experiments on synthetic data showed that FRIPS-SMOTE-FRBPS outperforms state-of-the-art methods such as SMOTE and its various modifications. Zheng et al [38] proposed a new oversampling approach SNOCC that can compensate the defects of SMOTE. In this proposed SNOCC, the authors increased the number of seed samples to rule out the new samples from the line segment between two seed samples in SMOTE.…”

Section: Related Workmentioning

confidence: 99%

“…We use a Laplace estimator to calculate the prior probability. The Laplace estimator shows excellent performance in Naive-Bayes classification algorithm [38,51]. One extra benefit of using Laplace estimator is that zero probability can be avoided.…”

Section: Case 2: Using F-measure Performance Metricmentioning

confidence: 99%

See 1 more Smart Citation

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

Hussein¹,

Li²,

Yohannese³

et al. 2019

IJCIS

View full text Add to dashboard Cite

Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector rather than data space. However, many recent works have shown that the imbalanced ratio in itself is not a problem and deterioration of the model performance is caused by other reasons linked to the minority class sample distribution. The blind oversampling by SMOTE leads to two major problems: noise and borderline examples. Noisy examples are those from one class located in the safe zone of the other. Borderline examples are those located in the neighborhood of the class boundary. These samples are associated with deteriorating performance of the models developed. Therefore, it is critical to concentrate on the minority class data structure and regulate the positioning of the newly introduced minority class samples for better performance of classifiers. Hence, this paper proposes the advanced SMOTE, denoted as A-SMOTE, to adjust the newly introduced minority class examples based on distance to the original minority class samples. To achieve this objective, we first employ the SMOTE algorithm to introduce new samples to the minority and eliminate those examples that are closer to the majority than the minority. We apply the proposed method to 44 datasets at various imbalance ratios. Ten widely used data sampling methods selected from the literature are employed for performance comparison. The C4.5 and Naive Bayes classifiers are utilized for experimental validation. The results confirm the advantage of the proposed method over the other methods in almost all the datasets and illustrate its suitability for data preprocessing in classification tasks.

show abstract

An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification

Cited by 33 publications

References 23 publications

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

SetConv: A New Approach for Learning from Imbalanced Data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

Contact Info

Product

Resources

About