UFFizi: a generic platform for ranking informative features

Background: Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis. Numerous supervised methods have been suggested but only a few unsupervised ones exist. Unsupervised Feature Filtering (UFF) is such a method, based on an entropy measure of Singular Value Decomposition (SVD), ranking features and selecting a group of preferred ones. Results: We analyze the statistical properties of UFF and present an efficient approximation for the calculation of its entropy measure. This allows us to develop a web-tool that implements the UFF algorithm. We propose novel criteria to indicate whether a considered dataset is amenable to feature selection by UFF. Relying on formalism similar to UFF we propose also an U...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Assaf Gottlieb, Roy Varshavsky, Michal Linial, David Horn
• Keywords: BMCBI 2010, Entropy Measure, Important Pre-processing Task, UFF Algorithm