Hierarchical feature selection for biological data
W. Jacak, K. Pröll - Hierarchical feature selection for biological data - Proceedings of the 26th European Modeling and Simulation Symposium EMSS 2014, Bordeaux, Frankreich, 2014, pp. 93-99
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.
- Univ. Prof. Dipl.-Ing. Dr. Witold Jacak
- FH-Prof. DI Dr. Karin Pröll