Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios-Reference-Cited by-同舟云学术

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Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios

Published:2024-02-09 Issue: Volume:6 Page:774-779
ISSN:
Container-title:2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)
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Author:

Das Surajit¹,Nayak Samaleswari P.²,Sahoo Biswajit¹,Nayak Sarat Chandra³

Affiliation:

1. KIIT Deemed to be University,School of Computer Engineering,Bhubaneswar,India

2. Silicon Institute of Technology,Dept. of Computer Science and Engineering,Bhubaneswar,India

3. GITAM Deemed to be University,School of Technology,Department of Computer Science and Engineering,Hyderabad,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10481518/10481530/10481583.pdf?arnumber=10481583

Reference18 articles.

1. On Supervised Class-Imbalanced Learning: An Updated Perspective and Some Key Challenges

2. Issues and challenges of class imbalance problem in classification

3. A systematic review for class-imbalance in semi-supervised learning

4. Adjusting the imbalance ratio by the dimensionality of imbalanced data

5. A Comparative Analysis on Recent Methods for Addressing Imbalance Classification

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