Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis
- 13 August 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 4 (3), 485-495
- https://doi.org/10.1109/tcbb.2007.1012
Abstract
In this paper, the recently developed Extreme Learning Machine (ELM) is used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima, improper learning rate and overfitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multicategory classification performance of ELM on three benchmark microarray data sets for cancer diagnosis, namely, the GCM data set, the Lung data set, and the Lymphoma data set. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN, and Support Vector Machine methods like SVM-OVO and Ramaswamy's SVM-OVA. ELM also achieves better accuracies for classification of individual categories.Keywords
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