Applied multivariate statistical analysis
Applied multivariate statistical analysis
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Noise reduction of cDNA microarray images using complex wavelets
IEEE Transactions on Image Processing
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
International Journal of Data Mining and Bioinformatics
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One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant Analysis (ULDA) for classifying gene expression data.