Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Feature extraction and dimensionality reduction for mass spectrometry data
Computers in Biology and Medicine
A rough set approach to feature selection based on power set tree
Knowledge-Based Systems
A new feature selection algorithm based on binomial hypothesis testing for spam filtering
Knowledge-Based Systems
Feature extraction for mass spectrometry data
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
Semantically-grounded construction of centroids for datasets with textual attributes
Knowledge-Based Systems
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Mass spectrometry data have high dimensionality. Dimensionality reduction is a very important step to greatly improve the performance of distinguishing cancer tissue from normal tissue. In this study multilevel wavelet analysis is performed on high dimensional mass spectrometry data. A set of orthogonal wavelet basis of approximation coefficients is extracted to reduce dimensionality of mass spectra and represent main components of mass spectrometry data. The best level of wavelet decomposition of mass spectrometry data is selected based on energy distribution of approximation coefficients. Compared to traditional principal component analysis (PCA) method, which dependents on training samples to build feature space, our proposed method is using wavelet basis to extract main components of mass spectrometry, keeping local properties of data, and computing efficiently. Experiments are conducted on three datasets. The competitive performance is achieved compared to other methods of feature extraction and feature selection.