Clustering and classification for dry bean feature imbalanced data

Clustering and classification for dry bean feature imbalanced data

Abstract The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the maj...

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Bibliographic Details
Main Authors: Chou-Yuan Lee, Wei Wang, Jian-Qiong Huang
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
K-means
BLSMOTE
Decision tree
Random forest
Support vector machine
Imbalanced data
Online Access:https://doi.org/10.1038/s41598-024-82253-6
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https://doi.org/10.1038/s41598-024-82253-6

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