Ajou University repository

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstructionoa mark
Citations

SCOPUS

0

Citation Export

Publication Year
2023-07-01
Journal
Bioengineering
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Bioengineering, Vol.10 No.7
Keyword
clusteringdimension reductionentropyfeature scoringfeature selectioninformation theorylow-dimensional embeddingmutual information (MI)principal component analysis (PCA)reconstruction error
All Science Classification Codes (ASJC)
Bioengineering
Abstract
Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.
ISSN
2306-5354
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/33568
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166351227&origin=inward
DOI
https://doi.org/2-s2.0-85166351227
Journal URL
www.mdpi.com/journal/bioengineering
Type
Article
Funding
This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(NRF-2022R1A2C1007434), and also by the Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00255968) grant funded by the Korea government (MSIT).
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Kim, So Yeon Image
Kim, So Yeon김소연
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download