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Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learning
Li, Tengyue1; Fong, Simon2; Mohammed, Sabah3; Fiaidhi, Jinan4; Guan, Steven5; Chang, Victor6
2022-08-01
Source PublicationFuture Generation Computer Systems
ISSN0167-739X
Volume133Pages:10-22
Abstract

In the medical domain, data are often collected over time, evolving from simple to refined categories. The data and the underlying structures of the medical data as to how they have grown to today's complexity can be decomposed into crude forms when data collection starts. For instance, the cancer dataset is labeled either benign or malignant at its simplest or perhaps the earliest form. As medical knowledge advances and/or more data become available, the dataset progresses from binary class to multi-class, having more labels of sub-categories of the disease added. In machine learning, inducing a multi-class model requires more computational power. Model optimization is enforced over the multi-class models for the highest possible accuracy, which of course, is necessary for life-and-death decision making. This model optimization task consumes an extremely long model training time. In this paper, a novel strategy called Group-of-Single-Class prediction (GOSC) coupled with majority voting and model transfer is proposed for achieving maximum accuracy by using only a fraction of the model training time. The main advantage is the ability to achieve an optimized multi-class classification model that has the highest possible accuracy near to the absolute maximum, while the training time could be saved by up to 70%. Experiments on machine learning over liver dataset classification and deep learning over COVID19 lung CT images were tested. Preliminary results suggest the feasibility of this new approach.

KeywordAlgorithm Classification Model Training Deep Learning Machine Learning Medical Dataset Multi-class Classification Parameter Optimization Radiological Images Recognition
DOI10.1016/j.future.2022.02.022
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000806791200002
Scopus ID2-s2.0-85126595008
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorLi, Tengyue; Fong, Simon; Mohammed, Sabah; Fiaidhi, Jinan; Guan, Steven; Chang, Victor
Affiliation1.Data Analytics and Collaborative Computing Laboratory, University of Macau, China
2.Department of Computer and Information Science, University of Macau, China
3.Department of Computer Science, Lakehead University, Thunder Bay, Canada
4.Department of Biotechnology, Lakehead University, Thunder Bay, Canada
5.Xl'an Jiaotoing-Liverpool University, Suzhou, China
6.Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, UK, United Kingdom
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li, Tengyue,Fong, Simon,Mohammed, Sabah,et al. Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learning[J]. Future Generation Computer Systems,2022,133:10-22.
APA Li, Tengyue,Fong, Simon,Mohammed, Sabah,Fiaidhi, Jinan,Guan, Steven,&Chang, Victor.(2022).Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learning.Future Generation Computer Systems,133,10-22.
MLA Li, Tengyue,et al."Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learning".Future Generation Computer Systems 133(2022):10-22.
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