Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Protein Folding Class Predictor for SCOP: Approach Based on Global Descriptors
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
An introduction to variable and feature selection
The Journal of Machine Learning Research
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Some new features for protein fold prediction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Feature selection based on mutual correlation
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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Feature selection is very important procedure in many pattern recognition problems. It is effective in reducing dimensionality, removing irrelevant data, and increasing accuracy of a classifier. In our previous work we propose a classifier combining the support vector machine (SVM) classifier with regularized discriminant analysis (RDA) classifier used to protein fold recognition problem. However high dimensionality of the feature vectors and small number of samples in the training data set caused that the problem is ill-posed for an RDA classifier and the feature selection is crucible for the accuracy of the classifier. In this paper we propose a simple and effective algorithm based on the class similarity which solves our problem and helps us to achieve very good acuracy on a real-world data set.