Binary classification of cancer microarray gene expression data using extreme learning machines
作者： C. Arun Kumar , S. Ramakrishnan
DOI: 10.1109/ICCIC.2014.7238297
关键词:
摘要: This paper presents the usage of Extreme Learning Machines for cancer microarray gene expression data. overcomes problems overfitting, local minima and improper training rate that are most common in traditional algorithms. We have evaluated binary classification performance on five bench marked datasets data namely ALL/AML, CNS, Lung Cancer, Ovarian Cancer Prostate Cancer. Feature Extraction has been performed using Correlation Coefficient prior to classification. The results indicate ELM produces comparable or better compared methods like Naive Bayes, Bagging, Random Forest Decision Table.
amrita.edu参考文章(17)
M. Hall, Correlation-based Feature Selection for Machine Learning PhD Thesis, Waikato Univer-sity. ,(1998)
C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter, V. de Schaetzen, R. Duque, H. Bersini, A. Nowe, A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis IEEE/ACM Transactions on Computational Biology and Bioinformatics. ,vol. 9, pp. 1106- 1119 ,(2012) , 10.1109/TCBB.2012.33
Runxuan Zhang, G.-B. Huang, N. Sundararajan, P. Saratchandran, Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis IEEE/ACM Transactions on Computational Biology and Bioinformatics. ,vol. 4, pp. 485- 495 ,(2007) , 10.1109/TCBB.2007.1012
Hyunsoo Yoon, Cheong-Sool Park, Jun Seok Kim, Jun-Geol Baek, Algorithm learning based neural network integrating feature selection and classification Expert Systems With Applications. ,vol. 40, pp. 231- 241 ,(2013) , 10.1016/J.ESWA.2012.07.018
Jan Chorowski, Jian Wang, Jacek M. Zurada, Review and performance comparison of SVM- and ELM-based classifiers Neurocomputing. ,vol. 128, pp. 507- 516 ,(2014) , 10.1016/J.NEUCOM.2013.08.009
Yuguang Wang, Feilong Cao, Yubo Yuan, A study on effectiveness of extreme learning machine Neurocomputing. ,vol. 74, pp. 2483- 2490 ,(2011) , 10.1016/J.NEUCOM.2010.11.030
Li-Yeh Chuang, Hsueh-Wei Chang, Chung-Jui Tu, Cheng-Hong Yang, Improved binary PSO for feature selection using gene expression data Computational Biology and Chemistry. ,vol. 32, pp. 29- 38 ,(2008) , 10.1016/J.COMPBIOLCHEM.2007.09.005
P. Maji, C. Das, Relevant and Significant Supervised Gene Clusters for Microarray Cancer Classification IEEE Transactions on Nanobioscience. ,vol. 11, pp. 161- 168 ,(2012) , 10.1109/TNB.2012.2193590
Joaquim F Pinto da Costa, H Alonso, L Roque, A Weighted Principal Component Analysis and Its Application to Gene Expression Data IEEE/ACM Transactions on Computational Biology and Bioinformatics. ,vol. 8, pp. 246- 252 ,(2011) , 10.1109/TCBB.2009.61
S. Imoto, S. Miyano, A Top-r Feature Selection Algorithm for Microarray Gene Expression Data IEEE/ACM Transactions on Computational Biology and Bioinformatics. ,vol. 9, pp. 754- 764 ,(2012) , 10.1109/TCBB.2011.151