Asymptotical lower limits on required number of examples for learning boolean networks

Authors:
Osman Abul;Reda Alhajj;Faruk Polat
Affiliations:
Dept of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway;Dept of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept of Computer Engineering, Middle East Technical University, Ankara, Turkey
Venue:
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Year:
2006

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Abstract

This paper studies the asymptotical lower limits on the required number of samples for identifying Boolean Networks, which is given as Ω(logn) in the literature for fully random samples. It has also been found that O(logn) samples are sufficient with high probability. Our main motivation is to provide tight lower asymptotical limits for samples obtained from time series experiments. Using the results from the literature on random boolean networks, lower limits on the required number of samples from time series experiments for various cases are analytically derived using information theoretic approach.