Affiliation:
1. Department of Mathematics and Computer Science, Faculty of Science Chulalongkorn University Phayathai, Phatumwan, Bangkok, THAILAND
Abstract
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
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
Artificial Intelligence,General Mathematics,Control and Systems Engineering
Cited by
1 articles.
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1. Random forest algorithm using quartile-pattern bootstrapping for a class imbalanced problem;Proceedings of the 2023 5th International Conference on Image, Video and Signal Processing;2023-03-24