Automatic merchandise classifying method on the basis of binary word segmentation and support vector machine

Abstract

The invention discloses an automatic merchandise classifying method on the basis of binary word segmentation and a support vector machine. The method mainly includes: subjecting all merchandise titles in a training set to binary word segmentation processing to construct a feature word library; constructing merchandise classification sets, expressing the merchandise titles as specific vectors according to the feature word library, generating training data by the aid of the specific vectors and the merchandise classification sets, and performing parameter optimization on the training data by a sequential dual method to obtain optimal classification vectors; calculating inner products of the optimal classification vectors and the specific vectors expressed by titles of merchandises to be classified, and selecting the classification corresponding to the maximum inner product as classification which the merchandises belong to. The automatic merchandise classifying method solves the problems that a product feature information base is hard to construct, and an automatic merchandise classifying method is long in training time and unsatisfactory in effect due to a feature space construction in the prior art.

Description

technical field [0001] The present invention relates to the field of data mining, in particular to an automatic commodity classification method based on binary word segmentation and Support Vector Machine (Support Vector Machine, SVM, an automatic learning type classification algorithm). Background technique [0002] Data mining generally refers to the process of automatically searching for information with special relationships hidden in a large amount of data. Classification is an important part of data mining. [0003] With the rapid development of electronic information technology, data mining has penetrated into various fields, especially in the field of e-commerce, and an efficient automatic classification method of commodities is very important to manage the massive commodity information in e-commerce. At present, there are many automatic classification methods for commodities, such as: decision tree method based on logical rules, naive Bayesian or Bayesian network m...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide an automatic commodity classification method based on binary word segmentation and support vector machine, to solve the difficulty in constructing product feature information database and the training of automatic commodity classification method due to the structure of feature space in the prior art. The problem of long time and unsatisfactory effect

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