scholarly journals Highly Scalable Attribute Selection for Averaged One-Dependence Estimators

2014 ◽  
pp. 86-97 ◽  
Author(s):  
Shenglei Chen ◽  
Ana M. Martinez ◽  
Geoffrey I. Webb
Keyword(s):  
Attribute Selection ◽  
Selection For

Adaptive attribute selection for configurator design via Shapley value

2011 ◽  
Vol 25 (2) ◽  
pp. 185-195 ◽  
Author(s):  
Yue Wang ◽  
Mitchell M. Tseng
Keyword(s):  
Shapley Value ◽  
Attribute Selection ◽  
Product Configuration ◽  
Interactive Process ◽  
Numerical Examples ◽  
Probability Ranking Principle ◽  
Relevant Attribute ◽  
Selection For ◽  
Substantial Interest ◽  
Selection Of

AbstractConfigurators have been generally accepted as important tools to elicit customers' needs and find the matches between customers' requirements and company's offerings. With product configurators, product design is reduced to a series of selections of attribute values. However, it has been acknowledged that customers are not patient enough to configure a long list of attributes. Therefore, making every round of configuring process productive and hence reducing the number of inputs from customers are of substantial interest to academic and industry alike. In this paper, we present an efficient product configuration approach by incorporating Shapley value, which is a concept used in game theory, to estimate the usefulness of each attribute in the configurator design. This new method iteratively selects the most relevant attribute that can contribute most in terms of information content from the remaining pool of unspecified attributes. As a result from product providers' perspective, each round of configuration can best narrow down the choices with given amount of time. The selection of the next round query is based on the customer's decision on the previous rounds. The interactive process thus runs in an adaptive manner that different customers will have different query sequences. The probability ranking principle is also exploited to give product recommendation to truncate the configuration process so that customers will not be burdened with trivial selection of attributes. Analytical results and numerical examples are also used to exemplify and demonstrate the viability of the method.

Shapley Value Based Attribute Selection for Configurators

2009 ◽  
Author(s):  
Yue Wang ◽  
Mitchell M. Tseng
Keyword(s):  
Shapley Value ◽  
Time Horizon ◽  
Attribute Selection ◽  
Product Configuration ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Relevant Attribute ◽  
Selection For ◽  
Tangible Product ◽  
Product Specification

Configurators have been generally accepted as important tools to interact with customers and elicit their requirements in the form of tangible product specification. These interactions, commonly called product configuring process, aim to find the best match between customers’ requirements and company’s offerings. Therefore an efficient configurator should take both product structure and customers’ preferences into consideration. In this paper, we present a novel iterative method of attributes selection for product configuring procedure. The algorithm is based on Shapley value, a concept used in game theory to estimate the usefulness of certain entities. It iteratively selects the most relevant attribute from the remaining attributes pool and proposes it for customers to configure. Thus it obtains customers’ specification in an adaptive manner in the sense that different customers may have different query sequences. Information content is used as the measure of usefulness. As a result, the most uncertainty can be eliminated and product development team has a better understanding of what customers want in a fix time horizon. Maximum a posteriori criterion is also exploited to give product recommendation based on the partially configured product configuration. Thus the customized 1-to-1 configuring procedure is presented and the recommendation can converge to a customer’s target with fewer interactions between the customers and designers. We also use a case of PC configurator to exemplify and test the viability of the presented method.

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