PCaLDI: Explainable Similarity and Distance Metrics Using Principal Component Analysis Loadings for Feature Importance-Reference-Cited by-同舟云学术

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PCaLDI: Explainable Similarity and Distance Metrics Using Principal Component Analysis Loadings for Feature Importance

Published:2024 Issue: Volume:12 Page:52623-52640
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nakanishi Takafumi¹^ORCID

Affiliation:

1. Department of Data Science, Musashino University, Koto-ku, Tokyo, Japan

Funder

Department of Data Science, Musashino University, Tokyo, Japan

Japan Science and Technology Agency, Tokyo, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10496693.pdf?arnumber=10496693

Reference69 articles.

1. Supervised Classification Algorithms in Machine Learning: A Survey and Review

2. Machine Learning Algorithms - A Review

3. A survey on XAI and natural language explanations

4. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

5. Efficient XAI techniques: A taxonomic survey;Chuang;arXiv:2302.03225,2023

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