A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering-Reference-Cited by-同舟云学术

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A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

Published:2021-10-17 Issue: Volume: Page:
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Container-title:2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Yan Xuyang,Sarkar Mrinmoy,Gebru Biniam,Nazmi Shabnam,Homaifar Abdollah

Funder

Air Force Research Laboratory

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9658572/9658575/09659208.pdf?arnumber=9659208

Reference39 articles.

1. A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

2. An effective and efficient hierarchical K-means clustering algorithm

3. Similarity-driven cluster merging method for unsupervised fuzzy clustering;xiong,2012

4. A novel clustering algorithm based on fitness proportionate sharing

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1. Leveraging Local Density Decision Labeling and Fuzzy Dependency for Semi-supervised Feature Selection;International Journal of Fuzzy Systems;2024-05-26

2. An Unsupervised Online Streaming Feature Selection Algorithm with Density Peak Clustering;2023 IEEE International Conference on Networking, Sensing and Control (ICNSC);2023-10-25

3. Feature Selection-Based Low-Voltage AC Arc Fault Diagnosis Method;IEEE Transactions on Instrumentation and Measurement;2023

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