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Binary classification of cancer microarray gene expression data using extreme learning machines
The results indicate that ELM produces comparable or better results compared to the traditional classification methods like Naïve Bayes, Bagging, Random Forest and Decision Table. Expand
Morphological Analyzer for Agglutinative Languages Using Machine Learning Approaches
This new and state of the art machine learning approach based on sequence labeling and training by kernel methods captures the non-linear relationships in the different aspect of morphological features of natural languages in a better and simpler way. Expand
A Comparative Performance Evaluation of Supervised Feature Selection Algorithms on Microarray Datasets
KNN classifier is found to produce higher classifier accuracy compared to traditional classifiers available in literature and fuzzy rough set based feature selection approach is computationally faster and produces lesser number of genes in the reduced subset compared to correlation based filter. Expand