Genetic Algorithm and Fuzzy C-Means Based Multi-Voting Classification Scheme in Data Mining

Autor:	Yubao Chen, Elsayed A. Orady, Mingwen Ou
Rok vydání:	2005
Předmět:	Artificial neural network Heuristic (computer science) business.industry Computer science media_common.quotation_subject Decision tree Machine learning computer.software_genre Fuzzy logic Weighting Data set Voting Genetic algorithm Artificial intelligence Data mining business computer media_common
Zdroj:	NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.
DOI:	10.1109/nafips.2005.1548537
Popis:	This paper presents a practical scheme used in data mining for classifications based on fuzzy logic and multivoting decision algorithms. It combines the information gain heuristic and genetic algorithm (GA) to minimize the uncertainty level when estimating the weighting functions used in the multiple voting decision scheme. A preliminary test of this scheme using a well-know data set demonstrated its competency and performance improvement for classifications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::dc9cfa53e679f51b811740786ad2503d https://doi.org/10.1109/nafips.2005.1548537 Zobrazit plný text záznamu