Mode Classification Based on Decision-Tree-Based Support Vector Machine in the Inventory Control

In the inventory decision support system, IDSS, the automatic selecting mode of inventory control relies on reasonable classification of commodity. Since rational application of inventory control model rests with so many factors, such as demand, supply and etc., present ABC and CVA classification methods are obviously insufficient to solve commodities classification problems. In this paper, least squares Support Vector Machines, SVM, are firstly adopted to construct a classifier solving inventory goods classification. It shows higher classification speed and reasonable classification result in the practical running. Further more, this method solved the dynamic classification problem.