Intelligent Partitioning for Feature Selection

Published Online:https://doi.org/10.1287/ijoc.1040.0104

References

  • Aha D. W., Bankert R. L., Fisher D., Lenz J.-H. A comparative evaluation of sequential feature selection algorithms. Artificial Intelligence and Statistics V (1996) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Basu A. Perspectives on operations research in data and knowledge management. Eur. J. Oper. Res. (1998) 111:1–14CrossrefGoogle Scholar
  • Blake C. L., Merz C. J. UCI repository of machine learning databases. (1998) . http://www.ics.uci.edu/~mlearn/MLRepository.html. Department of Information and Computer Science, University of California, Irvine, CAGoogle Scholar
  • Bradley P. S., Fayyad U. M., Mangasarian O. L. Mathematical programming for data mining: Formulations and challenges. INFORMS J. Comput. (1999) 11:217–238LinkGoogle Scholar
  • Bradley P. S., Mangasarian O. L., Street W. N. Feature selection via mathematical programming. INFORMS J. Comput. (1998) 10:209–217LinkGoogle Scholar
  • Caruana R., Freitag D. Greedy attribute selection. Proc. 11th Internat. Conf. Mach. Learn., New Brunswick, NJ (1994) (Morgan Kaufmann, San Francisco, CA) 28–36CrossrefGoogle Scholar
  • Fayyad U. M., Irani K. B. Multi-interval discretisation of continuous-valued attributes. Proc. 13th Internat. Joint Conf. Artificial Intelligence, Chambery, France (1993) (Morgan Kaufmann, San Francisco, CA) 1022–1027Google Scholar
  • Hall M. A. Correlation-based feature selection for discrete and numeric class machine learning. Proc. 17th Internat. Conf. Mach. Learn, Stanford University, Stanford, CA (2000) (Morgan Kaufmann, San Francisco, CA) 359–366Google Scholar
  • Kim Y. S., Street W. N., Menczer F. Feature selection in unsupervised learning via evolutionary search. Proc. 6th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, Boston, MA (2000) (ACM, New York) 365–369CrossrefGoogle Scholar
  • Liu H., Motoda H.Feature Extraction, Construction and Selection: A Data Mining Perspective (1998) (Kluwer Academic Publishers, Boston, MA) CrossrefGoogle Scholar
  • Modrzejewski M., Brazdil P. B. Feature selection using rough sets theory. Proc. Eur. Conf. Mach. Learn., Vienna, Austria (1993) (Springer, Berlin, Germany) 213–226CrossrefGoogle Scholar
  • Narandra P. M., Fukunaga K. A branch and bound algorithm for feature subset selection. IEEE Trans. Comput. (1977) 26:917–922CrossrefGoogle Scholar
  • Ólafsson S. Iterative ranking and selection for large-scale optimization. Proc. 1999 Winter Simulation Conf., Phoenix, AZ (1999) (IEEE, Piscataway, NJ) 479–485CrossrefGoogle Scholar
  • Ólafsson S., Shi L. Ordinal comparison via the nested partitions method. J. Discrete Event Dynam. Systems (2002) 12:211–239CrossrefGoogle Scholar
  • Quinlan J. R. Induction of decision trees. Mach. Learn. (1986) 1:81–106CrossrefGoogle Scholar
  • Shi L., Ólafsson S. Nested partitions method for global optimization. Oper. Res. (2000) 48:390–407LinkGoogle Scholar
  • Shih Y.-S. Families of splitting criteria for classification trees. Statist. Comput. (1999) 9:309–315CrossrefGoogle Scholar
  • Skalak D. Prototype and feature selection by sampling and random mutation hill climbing algorithms. Proc. 11th Internat. Conf. Mach. Learn., New Brunswick, NJ (1994) (Morgan Kaufmann, San Francisco, CA) 293–301CrossrefGoogle Scholar
  • Witten I. H., Frank E., Trigg L., Hall M., Holmes G., Cunningham S. J., Kasabov N., Ko K. Weka: Practical machine learning tools and techniques with Java implementations. Proc. ICONIP/ANZIIS/ANNES’99 Internat. Workshop: Emerging Knowledge Engrg. Connectionist-Based Inform. Systems, Dunedin, New Zealand (1999) (University of Otago, New Zealand) 192–196Google Scholar
  • Yang J., Honavar V., Motada H., Liu H. Feature subset selection using a genetic algorithm. Feature Selection, Construction, and Subset Selection: A Data Mining Perspective (1998) (Kluwer, New York) 117–136CrossrefGoogle Scholar
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