Mapping High Dimensional Sparse Customer Requirements into Product Configurations

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Jiao, Yao^1 ; Yang, Yu^1 ; Zhang, Hongshan^1

Department of Industrial Engineering, College of Mechanical Engineering, Chongqing University, Chongqing, China^1

关键词: Customer requirements;    Data mining process;    Domain knowledge;    High-dimensional;    Integrated method;    Local Linear Embedding;    Product configuration;    Uncertainty and complexity;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012022/pdf
DOI  :  10.1088/1757-899X/261/1/012022

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Mapping customer requirements into product configurations is a crucial step for product design, while, customers express their needs ambiguously and locally due to the lack of domain knowledge. Thus the data mining process of customer requirements might result in fragmental information with high dimensional sparsity, leading the mapping procedure risk uncertainty and complexity. The Expert Judgment is widely applied against that background since there is no formal requirements for systematic or structural data. However, there are concerns on the repeatability and bias for Expert Judgment. In this study, an integrated method by adjusted Local Linear Embedding (LLE) and Naïve Bayes (NB) classifier is proposed to map high dimensional sparse customer requirements to product configurations. The integrated method adjusts classical LLE to preprocess high dimensional sparse dataset to satisfy the prerequisite of NB for classifying different customer requirements to corresponding product configurations. Compared with Expert Judgment, the adjusted LLE with NB performs much better in a real-world Tablet PC design case both in accuracy and robustness.