Fabiyi, SD orcid.org/0000-0001-9571-2964 and Ezechukwu, DN (2022) Feature Extraction and Dimensionality Reduction of Cancer Data Using Folded LDA. In: 2022 3rd International Informatics and Software Engineering Conference (IISEC). 3rd International Informatics and Software Engineering Conference, 15-16 Dec 2022, Ankara, Turkey. IEEE ISBN 978-1-6654-5996-9
Abstract
Linear Discriminant Analysis is a less commonly applied dimensionality reduction technique in cancer data classification. This could be due to the inability of LDA to achieve good classification results when applied on small training data-a common characteristics of cancer data. F-LDA is an extension of LDA and was recently proposed in another application to overcome the challenge posed by the lack of enough samples for training. This paper therefore evaluates the effectiveness of F-LDA as a dimensionality reduction technique in cancer data classification. Experimental results obtained are promising and demonstrate the ability of F-LDA to effectively reduce the dimensionality of cancer data in small training sample scenarios.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jan 2023 17:13 |
Last Modified: | 24 Jan 2023 17:13 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IISEC56263.2022.9998312 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194519 |