dc.description.abstract | With rapid changes of business systems, both domestic and foreign industrial manufacturers face the problem of fast flow and various market demands. As a result, they are looking for the solution which could increase the effectiveness of cooperative product design. Due to the two production strategies, which are “low quantity” and “various”, the support department will design a variety of products with a small quantity. Also, its resource allocation and cooperative process supervision will become more and more important.
Academics provide numerous predictive theories and techniques for the resource allocation of government department. However, there are less predictions for the field of computer industrial manufacturers. This research uses a lot of electronic resources from a case company to create difference single and multiple classifiers for predicable resource allocation. This is to identify the most suitable model for the demand of the company and we expect that the result of the research could be referred by academics.
For the experiments, the research uses the Weka data mining tool to conduct the experiments for different allocators. In order to find out the most effective dimension of the company, the research use the GA-based heuristic algorithm and information gain algorithm to pre-process the data first. Therefore, we could obtain the best predictive model of resource allocation.
From the experimental results, we observe that no matter single or multiple classifiers are used, the general performance of R.O.C. are all higher than 0.9, and the bagging based multiple classifiers based on CART got a higher accuracy, but performing feature selection has a little influence on the prediction of data collection. Therefore, the research recommends that when making a prediction of resource allocation, the company could use the classifier for the allocation strategy based on CART.
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