Anytime algorithm for feature selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2005, Issue , 2001, Pages 532-539

  Last, Mark ^a   Kandel, Abraham ^a   Maimon, Oded ^b   Eberbach, Eugene ^c  

^a University of South Florida   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

^c ACADIA UNIVERSITY   (Canada)

Author keywords

Anytime algorithms; Feature selection; Fuzzy information gain; Information theoretic network

Indexed keywords

ALGORITHMS; BENCHMARKING; ECONOMIC AND SOCIAL EFFECTS; INFORMATION THEORY; ROUGH SET THEORY;

ALGORITHM PERFORMANCE; ANY-TIME ALGORITHMS; BENCHMARK DATASETS; COMPUTATION TIME; FUZZY INFORMATION; IMPROVE PERFORMANCE; QUALITY OF RESULTS; RELEVANT FEATURES;

FEATURE EXTRACTION;

EID: 84957803501     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45554-X_66     Document Type: Conference Paper

References (15)

1. C.L. Blake and C.J. Merz, UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
2. T. M. Cover, Elements of Information Theory, Wiley, New York, 1991.
3. T. Dean and M. Boddy, An Analysis of Time-Dependent Planning, Proc. AAAI-88, pp.49-54, AAAI, 1988.
4. E. Eberbach, Expressiveness of $-Calculus: What Matters?, Proc. The Ninth Intern. Symp. on Intelligent Information Systems IIS’2000, Springer-Verlag, Bystra, Poland, June 2000.
5. E. Eberbach, Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents and DNA-Based Computing in $-Calculus – Revised Version, Proc. Congress on Evolutionary Computation CEC’2000, San Diego, CA, July 2000.
6. E.J. Horvitz, Reasoning about Beliefs and Actions under Computational Resource Constraints, Proc. of the 1987 Workshop on Uncertainty in AI, Seattle, Washington, 1987.
7. M. Last and O. Maimon, An Information-Theoretic Approach to Data Mining, Submitted to Publication, 1999.
8. M. Last and A. Kandel, Automated Perceptions in Data Mining, Proc. 1999 IEEE International Fuzzy Systems Conference, Seoul, Korea, pp. 190-197, 1999.
9. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, Kluwer, Boston, 1998.
10. O. Maimon, A. Kandel, and M. Last, Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases. In Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry, Eds. Springer- Verlag, London, 1999.
11. S. Russell and E. Wefald, Do the Right Thing: Studies in Limited Rationality, The MIT Press, 1991.
12. J.R. Quinlan, Induction of Decision Trees, Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
13. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
14. S. Zilberstein, Operational Rationality through Compilation of Anytime Algorithms, Ph.D. Dissertation, University of California at Berkeley, 1993.
15. S. Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine, vol. 17, no. 3, pp. 73-83, 1996.