Information processing feature extracting method

Abstract

The invention provides an information processing feature extracting method. The method comprises the following steps of: establishing a feature item set which comprises the original feature items; calculating feature items in each class and the weight of the class; sorting the calculated weights of the feature items from high to low in each class and extracting the first K feature items; and combining the feature items extracted from the classes and unifying a feature space. An information processing feature extracting algorithm is provided, and a method for extracting spatial features by using word frequency is implemented. In the process of selecting a feature extracting algorithm, the time and space complexity and the feature extracting effect of the algorithm are comprehensively considered, and the feature extracting algorithm which is simple and practicable is designed and implemented.

Description

technical field [0001] The invention relates to the field of information processing algorithms, and in particular to an information processing feature extraction method. Background technique [0002] The representation of text and the selection of feature items is a basic problem in text mining and information retrieval, which quantifies the feature words extracted from the text to represent text information. Transform them from an unstructured original text into structured information that can be recognized and processed by a computer, that is, scientifically abstract the text and establish its mathematical model to describe and replace the text. It enables the computer to realize the recognition of text through the calculation and operation of this model. Since text is unstructured data, if we want to mine useful information from a large amount of text, we must first convert the text into a processable structured form. At present, people usually use the vector space mode...

AI Technical Summary

Problems solved by technology

The disadvantage of mutual information is that the score is very affected by the edge probability of the term

The disadvantage of the information gain method is that it considers the case where the feature does not occur

There are two main reasons: 1) The amount of calculation for feature extraction is too large, and the efficiency of feature extraction is too low, and the efficiency of feature extraction directly affects the efficiency of the entire text classification system

2) The dimension of the feature vector generated after feature extraction is too high, and the weight of each feature word in the feature vector cannot be directly calculated

Images