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Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray data. In this work, we aim to develop an automated system for robust and reliable cancer diagnoses based on gene microarray data. Support vector machine classifiers outperform other popular classifiers, such as K nearest neighbours, naive Bayes, neural networks and decision tree, often to a remarkable...
In response to the ICMLA 2009 "Functional Clustering of Gene Expression Profiles in Human Cancers Challenge", we present a new dimension reduction approach that ranks features based on their localized discriminative power. The proposed method is based on a localized dimension reduction penalty added to the objective function for training a hyper basis function (hyper BF or generalized RBF)...
Microarray data of gene expression pattern provide useful information for the diagnosis of certain diseases. However the dimension of microarray data is always very high and the volume of samples is small. How to select informative genes remains a challenge. In this research, multiple support vector machine (MSVM) were designed for disease diagnosis. Each SVM was trained using a few gene features...
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