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Principal direction linear oracle for gene expression ensemble classification (2007)
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| Venue: | Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data (CIBIO07); 2007 Int. Joint Conference on Neural Networks (IJCNN07 |
| Citations: | 3 - 2 self |
BibTeX
@INPROCEEDINGS{Peterson07principaldirection,
author = {Leif E. Peterson and Matthew A. Coleman},
title = {Principal direction linear oracle for gene expression ensemble classification},
booktitle = {Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data (CIBIO07); 2007 Int. Joint Conference on Neural Networks (IJCNN07},
year = {2007}
}
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Abstract
Abstract—A principal direction linear oracle (PDLO) ensemble classifier for DNA microarray gene expression data is proposed. The common fusion-selection ensemble based on weighted trust for a specifier classifier was replaced with pairs of subclassifiers of the same type using PDLO to perform a linear hyperplane split of training and testing samples. The hyperplane split forming the oracle was based on rotations of principal components extracted from sets of filtered features in order to maximize the separation of samples between the pair of miniclassifiers. Eleven classifiers were evaluated for performance with and without PDLO implementation, which included k nearest neighbor (kNN), naïve Bayes classifier (NBC), linear discriminant analysis (LDA), learning vector quantization (LVQ1), polytomous logistic regression (PLOG), artificial neural networks (ANN), constricted particle swarm optimization (CPSO), kernel regression (KREG), radial basis function networks (RBFN), gradient descent support vector machines (SVMGD), and least squares support vector machines (SVMLS). PLOG resulted in the best performance when used as a base classifier for PDLO. The greatest performance for PLOG implemented with PDLO occurred for tenfold CV and 100 rotations of PC scores with fixed angles for hyperplane splits. Random rotation angles for hyperplane splits resulted in reduced performance when compared to rotations with fixed angles. I.
Keyphrases
principal direction linear oracle hyperplane split gene expression ensemble classification fixed angle common fusion-selection weighted trust pdlo implementation random rotation angle base classifier polytomous logistic regression particle swarm optimization eleven classifier linear discriminant analysis gradient descent support vector machine radial basis function network artificial neural network square support vector machine pc score principal component tenfold cv filtered feature vector quantization reduced performance dna microarray gene expression data bayes classifier linear hyperplane split
