A Random Forest with Minority Condensation and Decision Trees for Class Imbalanced Problems
| العنوان: | A Random Forest with Minority Condensation and Decision Trees for Class Imbalanced Problems |
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| المؤلفون: | Krung Sinapiromsaran, Suvaporn Homjandee |
| المصدر: | WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL. 16:502-507 |
| بيانات النشر: | World Scientific and Engineering Academy and Society (WSEAS), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Discrete mathematics, Class (set theory), ComputingMethodologies_PATTERNRECOGNITION, Artificial Intelligence, Control and Systems Engineering, Computer science, General Mathematics, Condensation, Decision tree, Random forest |
| الوصف: | Building an effective classifier that could classify a target or class of instances in a dataset from historical data has played an important role in machine learning for a decade. The standard classification algorithm has difficulty generating an appropriate classifier when faced with an imbalanced dataset. In 2019, the efficient splitting measure, minority condensation entropy (MCE) [1] is proposed that could build a decision tree to classify minority instances. The aim of this research is to extend the concept of a random forest to use both decision trees and minority condensation trees. The algorithm will build a minority condensation tree from a bootstrapped dataset maintaining all minorities while it will build a decision tree from a bootstrapped dataset of a balanced dataset. The experimental results on synthetic datasets apparent the results that confirm this proposed algorithm compared with the standard random forest are suitable for dealing with the binary-class imbalanced problem. Furthermore, the experiment on real-world datasets from the UCI repository shows that this proposed algorithm constructs a random forest that outperforms other existing random forest algorithms based on the recall, the precision, the F-measure, and the Geometric mean |
| تدمد: | 2224-2856 1991-8763 |
| DOI: | 10.37394/23203.2021.16.46 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::a3b5f7b16942db36bb4e2bd390c64021 https://doi.org/10.37394/23203.2021.16.46 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........a3b5f7b16942db36bb4e2bd390c64021 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22242856 19918763 |
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| DOI: | 10.37394/23203.2021.16.46 |