2014
EMSS 2014 Proceeding
Hierarchical feature selection for biological data
Authors: Witold Jacak, Karin Pröll
Abstract
In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.
I3M Scientific Sponsors
I3M Industrial Sponsors
I3M Media Sponsors
