Exact rule learning via Boolean compressed sensing

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 3, 2013, Pages 1802-1810

  Malioutov, Dmitry M ^a   Varshney, Kush R ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; SIGNAL RECONSTRUCTION;

CLASSIFICATION ACCURACY; EXACT RECOVERIES; GROUP TESTING; HEURISTIC SOLUTIONS; LINEAR PROGRAMMING RELAXATION; RULE LEARNING; RULE-BASED CLASSIFICATION SYSTEM; SPARSE SIGNALS;

RECOVERY;

EID: 84897531301     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Bertsimas, D., Chang, A., and Rudin, C. An integer optimization approach to associative classification. In Adv. Neur. Inf. Process. Syst. 25, pp. 269-277. 2012.
2. Candès, E. J. and Wakin, M. B. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2):21-30, Mar. 2008.
3. Clark, P. and Niblett, T. The CN2 induction algorithm. Mach. Learn., 3(4):261-283, Mar. 1989.
4. Cohen, W. W. Fast effective rule induction. In Proc. Int. Conf. Mach. Learn., pp. 115-123, Tahoe City, CA, Jul. 1995.
5. Cohen, W. W. and Singer, Y. A simple, fast, and effective rule learner. In Proc. Nat. Conf. Artif. Intell., pp. 335-342, Orlando, FL, Jul. 1999.
6. Davenport, T. H. and Harris, J. G. Competing on Analytics: The New Science of Winning. Harvard Business School Press, Boston, MA, 2007.
7. Dembczyński, K., Kotłowski, W., and Słowiń ski, R. ENDER: A statistical framework for boosting decision rules. Data Min. Knowl. Disc., 21(1):52-90, Jul. 2010.
8. Demiriz, A., Bennett, K. P., and Shawe-Taylor, J. Linear programming boosting via column generation. Mach. Learn., 46(1-3):225-254, Jan. 2002. (Pubitemid 34129970)
9. Du, D.-Z. and Hwang, F. K. Pooling Designs and Non-adaptive Group Testing: Important Tools for DNA Sequencing. World Scientific, Singapore, 2006.
10. Dyachkov, A. G. and Rykov, V. V. A survey of super-imposed code theory. Probl. Control Inform., 12(4):229-242, 1983.
11. Frank, A. and Asuncion, A. UCI machine learning repository, http://archive.ics.uci.edu/ml, 2010.
12. Friedman, J. H. and Popescu, B. E. Predictive learning via rule ensembles. Ann. Appl. Stat., 2(3):916-954, Sep. 2008.
13. Fry, C. Closing the gap between analytics and action. INFORMS Analytics Mag., 4(6):4-5, Nov./Dec. 2011.
14. Fürnkranz, J. Separate-and-conquer rule learning. Artif. Intell. Rev., 13(1):3-54, Feb. 1999.
15. Gilbert, A. C., Iwen, M. A., and Strauss, M. J. Group testing and sparse signal recovery. In Asilomar Conf. Signals Syst. Comp. Conf. Record, pp. 1059-1063, Pacific Grove, CA, Oct. 2008.
16. Issenberg, S. How president Obama's campaign used big data to rally individual voters. MIT Tech. Rev., 116(1):389, Jan./Feb. 2013.
17. Jawanpuria, P., Nath, J. S., and Ramakrishnan, G. Efficient rule ensemble learning using hierarchical kernels. In Proc. Int. Conf. Mach. Learn., pp. 161-168, Bellevue, WA, Jun.-Jul. 2011.
18. Kearns, M. J. and Vazirani, U. V. An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA, 1994.
19. Letham, B., Rudin, C., McCormick, T. H., and Madigan, D. Building interpretable classifiers with rules using Bayesian analysis. Technical Report 609, Dept. Stat., Univ. Washington, Dec. 2012.
20. Liu, J. and Li, M. Finding cancer biomarkers from mass spectrometry data by decision lists. J. Comp. Bio., 12(7):971-979, Sep. 2005. (Pubitemid 41384392)
21. Malioutov, D. and Malyutov, M. Boolean compressed sensing: LP relaxation for group testing. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 3305-3308, Kyoto, Japan, Mar. 2012.
22. Malyutov, M. The separating property of random matrices. Math. Notes, 23(1):84-91, 1978.
23. Marchand, M. and Shawe-Taylor, J. The set covering machine. J. Mach. Learn. Res., 3:723-746, Dec. 2002.
24. Mazumdar, A. On almost disjunct matrices for group testing. In Proc. Int. Symp. Alg. Comput., pp. 649-658, Taipei, Taiwan, Dec. 2012.
25. Natarajan, B. K. Sparse approximate solutions to linear systems. SIAM J. Comput., 24(2):227-234, 1995.
26. Quinlan, J. R. Simplifying decision trees. Int. J. Man-Mach. Studies, 27(3):221-234, Sep. 1987. (Pubitemid 18089906)
27. Rivest, R. L. Learning decision lists. Mach. Learn., 2(3):229-246, Nov. 1987.
28. Rückert, U. and Kramer, S. Margin-based first-order rule learning. Mach. Learn., 70(2-3): 189-206, Mar. 2008.
29. Valiant, L. G. Learning disjunctions of conjunctions. In Proc. Int. Joint Conf. Artif. Intell., pp. 560-566, Los Angeles, CA, Aug. 1985.
30. Varshney, K. R., Rasmussen, J. C., Mojsilović, A., Singh, M., and DiMicco, J. M. Interactive visual salesforce analytics. In Proc. Int. Conf. Inf. Syst., Orlando, FL, Dec. 2012.