The nature of statistical learning theory
The nature of statistical learning theory
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Introduction to the Special Issue on Meta-Learning
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pattern Classifier Design by Linear Programming
IEEE Transactions on Computers
On the Complexity of Gene Expression Classification Data Sets
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Artificial Intelligence in Medicine
Modeling Problem Transformations based on Data Complexity
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
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Supervised Machine Learning methods have been successfully applied for performing gene expression based cancer diagnosis. Characteristics intrinsic to cancer gene expression data sets, such as high dimensionality, low number of samples and presence of noise makes the classification task very difficult. Furthermore, limitations in the classifier performance may often be attributed to characteristics intrinsic to a particular data set. This paper presents an analysis of gene expression data sets for cancer diagnosis using classification complexity measures. Such measures consider data geometry, distribution and linear separability as indications of complexity of the classification task. The results obtained indicate that the cancer data sets investigated are formed by mostly linearly separable non-overlapping classes, supporting the good predictive performance of robust linear classifiers, such as SVMs, on the given data sets. Furthermore, we found two complexity indices, which were good indicators for the difficulty of gene expression based cancer diagnosis.