Title :
The Application of Optimized Fuzzy Decision Trees in Business Intelligence
Author :
Zhao, Ming-hua ; Chen, Yu-zhe ; Liu, Dong-rong ; Li, Jun
Author_Institution :
Coll. of Math. & Inf. Sci., Hebei Normal Univ.
Abstract :
Business intelligence is an important method to help managers make decision. In the management of enterprises, it is critical to analyze the customer forfeit crisis for rising competition ability, which is the important branch of business intelligence. On the basis of the advantage of fuzzy decision trees in solving the fuzziness of customer data compared with decision trees, this paper optimizes the two important parameters in fuzzy decision trees using genetic algorithm. Then an approach is proposed to analyze the customer forfeit crisis based on the optimized fuzzy decision tees. The experiment results show that this approach is feasible and effective, which provides a new idea for analyzing and forecasting the customer forfeit crisis and help managers make better decision in business strategy
Keywords :
competitive intelligence; customer relationship management; decision trees; fuzzy set theory; genetic algorithms; learning by example; FDT inductive learning; business intelligence; customer forfeit crisis forecasting; genetic algorithm; optimized fuzzy decision trees; Bismuth; Classification tree analysis; Crisis management; Cybernetics; Data analysis; Data mining; Decision trees; Educational institutions; Fuzzy sets; Genetic algorithms; Genetics; Information science; Learning systems; Machine learning; Mathematics; Optimization methods; Business intelligence; Customer forfeit crisis; Fuzzy decision tree; Genetic algorithm;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258660
