Paper Info
Title
SBC: A New Strategy for Multiclass Lung Cancer Classification Based on Tumour Structural Information and Microarray Data
Abstract
Lung cancer has different subtypes which are different in cell size and growth pattern. Correctly classifying subtypes of lung cancer can help design specific treatments to increase patient survival rate. In this work, we propose an innovative Structural Binary Classification (SBC) strategy for classifying lung cancer subtypes using microarray data. The strategy is based on Gene Expression Programming (GEP) algorithm. Classification performance evaluations and comparisons between our GEP based model and common binary decomposition strategies, as well as three representative machine learning methods, support vector machine, neural network and C4.5, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results showed that GEP model with our strategy outperformed other models in terms of accuracy, standard deviation and area under the receiver operating characteristic curve. The work provides a useful tool for lung cancer classification based on tumour structural information.
Year
DOI
Venue
2018
10.1109/ICIS.2018.8466448
2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)
Keywords
Field
DocType
Multiclass classification,lung cancer diagnosis,gene expression analysis,gene expression programming
Gene expression programming,Receiver operating characteristic,Binary classification,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Statistical classification,Cancer,Machine learning,Multiclass classification
Conference
ISBN
Citations 
PageRank 
978-1-5386-5893-2
0
0.34
References 
Authors
16
4
Name
Order
Citations
PageRank
Hasseeb Azzawi131.41
Jingyu Hou218116.93
Russul Alanni331.41
Yong Xiang4113793.92