Performing Effective Feature Selection by Investigating the Deep Structure of the Data

Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Volumn , Issue , 1996, Pages 379-383

  Richeldi, Marco ^a   Lanzit, Pier Luca ^a  

^a CSELT   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS;

DEEP STRUCTURE; EVALUATION FUNCTION; FEATURE CONFIGURATION; FEATURE SPACE; FEATURE SUBSET; FEATURES SELECTION; FEEDBACK MODEL; FILTER MODEL; HIGH ORDER CORRELATION; HIGHER ORDER CORRELATION;

FEATURE SELECTION;

EID: 85027110990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

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2. Vafai, H. and De Jong, K. 1992. Genetic algorithms as a tool for features selection in machine learning. In Proceedings of the 4th International Conference on Tools with Artificial Intelligence, 200-203.
3. Bohem, B. W. 1981. Software Engineering Economics. Prentice Hall.
4. Richeldi, M. and Rossotto, M. 1995. Class-Driven Statistical Discretization of Continuous Attributes. In Proceedings. of the 8th European Conf. of Machine Learning. Springer & Verlag.
5. Murphy, P. M. and Aha, D. W. 1994. UC1 repository of machine learning databases.
6. Michie, D.; Spieghalter, D. J. and Taylor, C. C. eds. 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood Publ.
7. Richeldi, M. and Rossotto, M. 1996. Combining Statistical Techniques and Search Heuristic to Perform Effective Feature Selection. To appear in Machine Learning and Statistics: the Zntegace.T aylor, C., and Nakhaeizadeh,R ., eds. John Wiley & Sons.