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2024 | OriginalPaper | Chapter

Nearest Centroid Classifier Based on Information Value and Homogeneity

Authors : Mehmet Hamdi Özçelik, Serol Bulkan

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

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Abstract

The chapter presents a novel Nearest Centroid classifier that leverages Information Value and homogeneity metrics to enhance distance calculations, resulting in improved performance across 50 datasets. The algorithm's superiority is demonstrated through extensive benchmarking against established classifiers like Nearest Centroid, Logistic Regression, K-Nearest Neighbors, and Decision Trees. The use of Information Value for predictive power and various homogeneity metrics for feature evaluation sets this algorithm apart, making it a promising tool for binary classification tasks. The authors highlight the algorithm's potential for further enhancement and its notable performance in credit risk domain datasets.

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Title: Nearest Centroid Classifier Based on Information Value and Homogeneity
Authors: Mehmet Hamdi Özçelik
Serol Bulkan
Publisher: Springer Nature Singapore
Book: Advances in Intelligent Manufacturing and Service System Informatics
Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-6062-0_5

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