Using rough sets with heuristics for feature selection

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

Volumn 1711, Issue , 1999, Pages 178-187

  Dong, Juzhen ^a   Zhong, Ning ^a   Ohsuga, Setsuo ^b  

^a YAMAGUCHI UNIVERSITY   (Japan)

^b WASEDA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; FILTRATION; HEURISTIC METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; ROUGH SET THEORY; SET THEORY; SOFT COMPUTING;

EVALUATION CRITERIA; EVALUATION FUNCTION; FILTER APPROACH; GREEDY HEURISTICS; INDUCTION ALGORITHMS; PRE-PROCESSING STEP; RULE DISCOVERY; WRAPPER APPROACH;

FEATURE EXTRACTION;

EID: 84958033390     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48061-7_22     Document Type: Conference Paper

References (11)

1. Z. Pawlak. ROUGH SETS, Theoretical Aspects of Reasoning about Data, Kluwer (1991).
2. A. Skowron and C. Rauszer. “The Discernibility Matrics and Functions in Information Systems”, in R. Slowinski (ed.) Intelligent Decision Support, Kluwer (1992) 331-362.
3. A. Skowron and L. Polkowski. “A Synthesis of Decision Systems from Data Tables”, in T.Y. Lin and N. Cercone (eds.) Rough Sets and Data Mining, Kluwer (1997) 259-299.
4. M. Boussouf. “A Hybrid Approach to Feature Selection”, J. Zytkow and M. Quafafou (eds.) Principles of Data Mining and Knowledge Discovery, LNAI 1510, Springer (1998) 231-238.
5. Y.Y. Yao, S.K.M. Wong, and C.J. Butz. “On Information-Theoretic Measures of Attribute Importance”, Zhong, N. and Zhou, L. (eds.) Methodologies for Knowledge Discovery and Data Mining, LNAI 1574, Springer (1999) 231-238.
6. H. Liu and H. Motoda. Feature Selection, Kluwer (1998).
7. B. Zupan, M. Bohanec, J. Demsar, and I. Bratko. “Feature Transformation by Function Decomposition”, IEEE Intelligent Systems, 13(2) (1998) 38-43.
8. A.V. Aho, J.E. Hopcroft, J.D. Ullman. Data Structures and Algorithms, Addison- Wesley (1983)
9. N. Zhong, J.Z. Dong, and S. Ohsuga, “Data Mining based on the Generalization Distribution Table and Rough Sets”, X. Wu et al. (eds.) Research and Development in Knowledge Discovery and Data Mining, LNAI 1394, Springer (1998) 360-373.
10. J.Z. Dong, N. Zhong, and S. Ohsuga, “Probabilistic Rough Induction: The GDTRS Methodology and Algorithms”, Z.W. Ras and A. Skowron (eds.) Foundations of Intelligent Systems, LNAI 1609, Springer (1999) 621-629.
11. R. Kohavi. “Useful Feature Subsets and Rough Set Reducts”, Proc. Third Inter. Workshop on Rough Set and Soft Computing (1994) 310-317.