Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Large engineering project risk management using a Bayesian belief network
Expert Systems with Applications: An International Journal
Cost-benefit factor analysis in e-services using bayesian networks
Expert Systems with Applications: An International Journal
A Kansei mining system for affective design
Expert Systems with Applications: An International Journal
Robust independence testing for constraint-based learning of causal structure
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Journal of Intelligent Manufacturing
| Hi-index | 12.05 |
The construct of ''feature fatigue'' represents the phenomenon of customer's inconsistent satisfaction: customers prefer to choose products with more features and capacities initially, but once actually worked with a product they will find the complex ones are too hard to use. Clearly, customer's dissatisfaction after use will have a negative effect on company's long-term revenue, and the inconsistence is a big challenge for firm's product development. Researchers have proposed some methods to ''defeat'' feature fatigue, however, most recent research just analyzes features one by one and ignore the relationships among them. Another problem is that the uncertain nature of customer preferences has not been paid enough attention. To solve these problems, a probability based methodology for feature fatigue analysis is proposed, in which Bayesian network techniques are used to represent the uncertain customer preferences for capacity and usability. And in this method, sensitivity analysis is implemented to identify the key features that affect feature fatigue most, and the relationships among features are analyzed using Bayesian network inference. An example is given to illustrate the usage of the proposed method in product development process.