Name country identification method based on WEB and GBBoosting algorithms

Abstract

The invention discloses a name country identification method based on WEB and GBBoosting algorithms, and belongs to the technical field of WEB data mining. The method comprises the steps of I. extracting names of scholars in universities through a WEB data extraction technology; II. constructing a GBBoosting algorithm: constructing weak classifiers, wherein each weak classifier outputs a weak classification hypothesis to an input sample, and a strong classifier is formed through weight fusion of all weak classifiers; and III. identifying countries of the names through the GBBoosting algorithm. The name country identification method based on WEB and GBBoosting algorithms disclosed by the invention effectively solves a problem on classifying names of two countries which are similar in the spelling way; and meanwhile, the method, compared to existing other classifying methods, is easier to implement, and can be better applied to engineering practices such as name country or city country semantic annotation.

Description

technical field [0001] The invention belongs to the technical field of WEB data mining, and in particular relates to a name and country recognition method based on WEB and GBBoosting algorithm. Background technique [0002] With the rapid development of the Internet and the increasing abundance of WEB resources, in order to quickly and accurately mine necessary and meaningful data from massive data information, in recent years, WEB semantic analysis technology and text classification technology have been widely used in the field of WEB data mining. Applications, WEB-based applications have changed users' living habits and working styles to some extent, and are also welcomed and appreciated by more and more users. [0003] Classification methods such as KNN and Bayesian have achieved good classification results in many classification fields. For example, Jiemei et al. applied KNN to the field of image processing, and proposed a KNN-based classification algorithm to detect the...

AI Technical Summary

Problems solved by technology

However, the classification accuracy of the above classification method in the scene of classification of names by country needs to be further improved, especially when the spelling of names in two countries is similar, the classification accuracy is only higher than random guessing

It can be seen that the above classification algorithm has great limitations in the application of the classification of personal names and countries

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