Correction-based K nearest neighbor text classification method

Abstract

The invention discloses a correction-based K nearest neighbor text classification method, which comprises the following steps of: pre-processing texts, namely performing word segmentation on each document in a training text set, removing stop words, and performing projectized expression on the texts; selecting text characteristics, namely reducing dimensions of text vectors, and selecting document characteristics closely related with a document theme concept as few as possible; and finally, performing classification by using a correction-based K nearest neighbor text classification algorithm construction classifier to obtain a classification result. The method has accurate classification result.

Description

technical field [0001] The invention belongs to the field of electronic resource information classification and retrieval, and relates to an unstructured text classification and management method, in particular to a modified K-nearest neighbor text classification method. Background technique [0002] In recent years, with the rapid development of information technology, especially the popularization of the Internet and the large-scale application of databases, the electronic resource information on the Internet has increased dramatically. Facing the information explosion and diversification of information, how to effectively organize and manage these massive information, And it has become a major problem to quickly and accurately obtain the information you need and are really interested in. As a key technology for organizing and processing a large amount of electronic resource information, text classification technology will help information retrieval and analysis, and facil...

AI Technical Summary

Problems solved by technology

From the 1960s to the end of the 1980s, knowledge engineering technology was the most important and most effective content text classification system during this period. It mainly used artificial methods to build classifiers, which was labor-intensive and prone to human errors.

There is obviously a problem in this kind of rule: when the sample distribution density is not uniform, only the order of the first K nearest neighbor samples is taken without considering their distance difference. Generally, the K nearest neighbors have classification decisions that tend to be large categories, which will reduce the Classification performance of the classifier

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