A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Microarray Classification from Several Two-Gene Expression Comparisons
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
A model for a complex polynomial SVM kernel
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
TC-VGC: A Tumor Classification System using Variations in Genes' Correlation
Computer Methods and Programs in Biomedicine
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Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer.