Performance analysis of a greedy algorithm for inferring Boolean functions

Information Processing Letters

Volumn 93, Issue 1, 2005, Pages 7-12

  Fukagawa, Daiji ^a   Akutsu, Tatsuya ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

Analysis of algorithms; Machine learning; Unbalanced function; Uniform distribution

Indexed keywords

ALGORITHMS; DATA MINING; FUNCTIONS; LEARNING SYSTEMS; PROBLEM SOLVING; RANDOM PROCESSES; SET THEORY;

ANALYSIS OF ALGORITHMS; DATA SET; UNBALANCED FUNCTION; UNIFORM DISTRIBUTION;

BOOLEAN ALGEBRA;

EID: 9944249082     PISSN: 00200190     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipl.2004.09.017     Document Type: Article

References (16)

1. T. Akutsu, F. Bao, Approximating minimum keys and optimal substructure screens, in: Proc. COCOON'96, in: Lecture Notes in Comput. Sci., vol. 1090, Springer, Berlin, 1996, pp. 290-299.
2. T. Akutsu, S. Miyano, S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the Boolean network model, in: Proc. Pacific Symp. in Biocomputing'99, 1999, pp. 17-28.
3. T. Akutsu, S. Miyano, S. Kuhara, A simple greedy algorithm for finding functional relations: Efficient implementation and average case analysis, Theoret Comput. Sci. 292 (2) (2003) 481-495, preliminary version has appeared in Discovery Science (2000) 86-98.
4. T. Akutsu, S. Miyano, S. Kuhara, A simple greedy algorithm for finding functional relations: Efficient implementation and average case analysis, Theoret Comput. Sci. 292 (2) (2003) 481-495, preliminary version has appeared in Discovery Science (2000) 86-98.
5. J. Arpe, R. Reischuk, Robust inference of relevant attributes, in: Proc. 14th Internat. Conf. Algorithmic Learning Theory 2003, in: Lecture Notes in Comput. Sci., vol. 2842, Springer, Berlin, 2003, pp. 99-113.
6. J. Arpe, R. Reischuk, Robust inference of relevant attributes, in: Proc. 14th Internat. Conf. Algorithmic Learning Theory 2003, in: Lecture Notes in Comput. Sci., vol. 2842, Springer, Berlin, 2003, pp. 99-113.
7. A. Blum, P. Langley, Selection of relevant features and examples in machine learning, Artif. Intell. 97 (1-2) (1997) 245-271.
8. E. Boros, T. Horiyama, T. Ibaraki, K. Makino, M. Yaghira, Finding essential attributes from binary data, Ann. Math. Artif. Intell. 39 (3) (2003) 223-257.
9. D. Fukagawa, T. Akutsu, Performance analysis of a greedy algorithm for inferring Boolean functions, in: Proc. 6th Internat. Conf. Discovery Science 2003, in: Lecture Notes in Comput. Sci., vol. 2843, Springer, Berlin, 2003, pp. 114-127.
10. D. Fukagawa, T. Akutsu, Performance analysis of a greedy algorithm for inferring Boolean functions, in: Proc. 6th Internat. Conf. Discovery Science 2003, in: Lecture Notes in Comput. Sci., vol. 2843, Springer, Berlin, 2003, pp. 114-127.
11. D. Gamberger, A minimization approach to prepositional inductive learning, in: Proc. 8th European Conf. on Machine Learning, in: Lecture Notes in Comput. Sci., vol. 912, Springer, Berlin, 1995, pp. 151-160.
12. H. Mannila, K.-J. Raiha, On the complexity of inferring functional dependencies, Discrete Appl. Math. 40 (1992) 237-243.
13. E. Mossel, R. O'Donnell, R.P. Servedio, Learning juntas, in: Proc. the 35th ACM Symp. on Theory of Computing, ACM Press, New York, 2003, pp. 206-212.
14. R. Motwani, P. Raghavan, Randomized Algorithms, Cambridge University Press, Cambridge, 1995.
15. C.-C. Tsai, M. Marek-Sadowska, Boolean matching using generalized reed-muller forms, in: Proc. 31st Conference on Design Automation, ACM Press, New York, 1994, pp. 339-344.
16. V. Vazirani, Approximation Algorithms, Springer, Berlin, 2001.