Decision Tree Algorithm with Class Overlapping-Balancing Entropy for Class Imbalanced Problem

Sagoolmuang, Artit; Sinapiromsaran, Krung

doi:10.18178/ijmlc.2020.10.3.955

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [40] derives weight from the class distribution to improve the minority class importance. Reference [41] introduces the overlapping weighting function to improve minority class importance. However, heuristic judgments, i.e., determined by experts is not rigorous, and grid search is time-consuming and laborious.…”

Section: Datasets Descriptionmentioning

confidence: 99%

A Machine Learning Based Funding Project Evaluation Decision Prediction

Zhang¹,

Yao²,

Hu³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human mind. In this paper, we apply machine learning to the field of funding allocation decision making, and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision? and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding? Since funding data is characterized by imbalanced data distribution, we propose a slacked weighted entropy decision tree (SWE-DT). We assign weight to each class with the help of slacked factor. The experimental results show that the SWE decision tree performs well with sensitivity of 0.87, specificity of 0.85 and average accuracy of 0.75. It also provides a satisfied classification accuracy with Area Under Curve (AUC) = 0.87. This implies that the proposed method accurately classified minority class instances and suitable to imbalanced datasets. By adding evaluator factors into the model, sensitivity is improved by over 9%, specificity improved by nearly 8% and the average accuracy also increased by 7%. It proves the feasibility of using evaluators' characteristics as predictors. And by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators, it enriches the literature in the field of decision making and machine learning field.

show abstract

Section: Datasets Descriptionmentioning

confidence: 99%

A Machine Learning Based Funding Project Evaluation Decision Prediction

Zhang¹,

Yao²,

Hu³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Many techniques in this approach are an oversampling technique [9,10,11] which synthesizes random instances from the minority group avoiding those from majority groups, or an undersampling technique [12,13] which discards random instances from the majority group to extend the minority region of instances in the minority class or the mixture of an oversampling and undersampling technique [14]. Second, an algorithmic-level methodology upgrades or reimplements the classification algorithms to be more robust to noise while handling minority instances successfully [15,16,17,18,19]. Third, the hybrid methodology combines both the data-level approach and the algorithmic-level approach such as Adaboost [20], Boosting [21], Bagging [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Mass Ratio Variance Majority Undersampling and Minority Oversampling Technique for Class Imbalance

Polvimoltham

Sinapiromsaran

2021

Frontiers in Artificial Intelligence and Applications

Self Cite

View full text Add to dashboard Cite

A sampling method is one of the popular methods to deal with an imbalance problem appearing in machine learning. A dataset having an imbalance problem contains a noticeably different number of instances belonging to different classes. Three sampling techniques are used to solve this problem by balancing class distributions. The first one is an undersampling technique removing noises from a class having a large number of instances, called a majority class. The second one is an over-sampling technique synthesizing instances from a class having a small number of instances, called a minority class, and the third one is the combined technique of both undersampling and oversampling. This research applies the combined technique of both undersampling and oversampling via the mass ratio variance scores of instances from each individual class. For the majority class, instances with high mass ratio variances are removed whereas for the minority class, instances with high mass ratio variances are used in synthesizing minority instances. The results of this proposed sampling technique help improve recall over standard classifiers: a decision tree, a random forest, Linear SVM, MLP on all synthesized datasets; however it may have low precision. So the combined measure of precision and recall is used, F1-score. Recall and F1-scores of synthesized datasets and UCI datasets are significantly better for collections of datasets having small imbalance ratio. Moreover, the Wilcoxon signed-rank test is used to confirm the improvement for datasets having imbalance ratio smaller than or equal to 0.2.

show abstract

“…There are many techniques in this approach such as an oversampling technique [12,13,14] which synthesises instances randomly from the minority class while ignore those from majority class, or an undersampling technique [15,16] which randomly removed instances from the majority class to expand the minority region of instances in the minority class or the combines of both an oversampling and undersampling technique [17]. Second, an algorithmic-level methodology enhances or reimplements the classification algorithms to be more resilient to noise while successfully handling minority instances [18,19,20,21,22]. An algorithmic-level methodology efficiently deals with the class imbalanced problem using the original data that is available without any modification.…”

Section: Techniques To Solve a Class Imbalance Problemmentioning

confidence: 99%

Mass ratio variance majority cleansing and minority oversampling technique for class imbalanced

Polvimoltham

View full text Add to dashboard Cite

A sampling method is one of the basic methods to deal with an imbalance problem appearing in machine learning. A dataset having an imbalance problem has a noticeably skewed distribution among different classes. There are three types of sampling techniques to solve this problem by balancing class distributions, undersampling technique, over-sampling technique, and combined sampling technique. In this research, the mass ratio variance scores of each data point of the same class are computed and used to remove noise from a majority class and synthesise instances from a minority class. The results of this proposed sampling technique improve recall over standard classifiers: a decision tree, a random forest, Linear SVM, and MLP on all synthesised datasets. Performances are reported on synthesised datasets and UCI datasets via three measures: Precision, Recall, and F1-score. Moreover, Wilcoxon signed-rank tests are used to confirm the improved performance.

show abstract

Decision Tree Algorithm with Class Overlapping-Balancing Entropy for Class Imbalanced Problem

Cited by 9 publications

References 22 publications

A Machine Learning Based Funding Project Evaluation Decision Prediction

A Machine Learning Based Funding Project Evaluation Decision Prediction

Mass Ratio Variance Majority Undersampling and Minority Oversampling Technique for Class Imbalance

Mass ratio variance majority cleansing and minority oversampling technique for class imbalanced

Contact Info

Product

Resources

About