On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Letters: Fully complex extreme learning machine
Neurocomputing
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Multi-category bioinformatics dataset classification using extreme learning machine
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A supervised combination strategy for illumination chromaticity estimation
ACM Transactions on Applied Perception (TAP)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers
Neural Processing Letters
Voting based extreme learning machine
Information Sciences: an International Journal
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Emotional sentence identification in a story
Proceedings of the Workshop at SIGGRAPH Asia
Diagnose the premalignant pancreatic cancer using high dimensional linear machine
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis
Neural Processing Letters
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In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct 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 multi-category classification performance of ELM on three benchmark microarray datasets for cancer diagnosis, namely, the GCM dataset, the Lung dataset and the Lymphoma dataset. 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.