We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class data sets with numerical attributes as pre-processing fo...
Frank Plastria, Steven De Bruyne, Emilio Carrizosa
Background: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard super...
We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Ou...
Abstract. Feature selection is an important task in data mining because it allows to reduce the data dimensionality and eliminates the noisy variables. Traditionally, feature selec...
We propose a new feature selection criterion not based on calculated measures between attributes, or complex and costly distance calculations. Applying a wrapper to the output of a...