Optimal Feature Selection for Multivalued Attributes Using Transaction Weights as Utility Scale-Reference-Cited by-同舟云学术

Optimal Feature Selection for Multivalued Attributes Using Transaction Weights as Utility Scale

Published:2018 Issue: Volume: Page:533-545
ISSN:2194-5357
Container-title:Proceedings of the Second International Conference on Computational Intelligence and Informatics
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Author:

Lnc Prakash K.,Anuradha K.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-8228-3_49

Reference38 articles.

1. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Norwell (1998).

2. Deng, H., Runger, G., Tuv, E.: Bias of Importance Measures for Multi-valued Attributes and Solutions. In: Honkela, T. (ed.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 293–300. Springer, Heidelberg (2011).

3. Dzeroski, S.: Multi-relational data mining: an introduction. SIGKDD Explorations Newsletter 5(1), 1–16 (2003).

4. Garriga, G.C., Khardon, R., De Raedt, L.: On mining closed sets in multi-relational data. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 804–809. Morgan Kaufmann Publishers Inc., San Francisco (2007).

5. Goethals, B., Page, W., Mampaey, M.: Mining interesting sets and rules in relational databases. In: Proceedings of the ACM Symposium on Applied Computing, pp. 997–1001. ACM, New York (2010).

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