Classification method based on feature library and projection

Abstract

The invention discloses a classification method based on feature library and projection, which derives from statistical approaches. It is found that the sample individuals in the same classification are more likely to have the same features than the samples in other classifications. Such features are often unique to the classification, and through the aggregation of the samples, a feature library can be formed so that a higher weight can be given to a feature item with the unique features. After a feature library is formed, for the samples to be classified and for the feature items with the samples, the same features are extracted from the classification feature library to form projection samples. The classification of a new sample is determined through the calculation of the similarity between the unknown classification sample and the projection samples.

Description

technical field [0001] The invention belongs to the technical fields of data mining, machine learning and pattern recognition, and in particular relates to a classification method based on feature library-projection. Background technique [0002] Classes are an important research area in data mining, machine learning, and pattern recognition. For example, in text management, classification technology can efficiently classify texts, which is convenient for people to manage and use. At present, the main classification techniques include Support Vector Algorithm (SVM), k-Nearest Neighbor Algorithm (k-NN) and so on. The SVM algorithm finds the support vector for dividing the hyperplane through training, and then completes the classification by calculating the relationship between the sample and the support vector; the k-NN algorithm requires almost no training, but finds the closest new sample to the unknown classification through calculation. There are k known classification ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a classification method based on feature library-projection, which aims to solve the problem that the current SVM pre-training calculation complexity is high, and k-NN has too much calculation in the classification process

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