Extensible self-adapting multi-core classification method

Abstract

A self-adaptive multi-core classification method relates to the field of artificial intelligence, especially to data mining technology. In a pre-processing phase, a multi-core matrix is obtained; in a modelling phase, a multi-core classifier relating to a cluster is built; in a parameter learning phase, classifier parameters and plural groups of multi-core weighting parameters are optimized in a uniform frame; in a data classifying phase, which cluster a sample to be classified belongs to is determined, and data are classified by the classifier having been studied. Correlation between classes and diversity in a class of complex data sets are mined by inducing medium expressing clusters in the invention; self adaptation relating to clusters and plural groups of multi-core classifiers are built; and the classifier parameters and plural groups of multi-core weighting parameters are optimized in a uniform studied frame by means of iteration. Problems on data classifications with many classes and complex features and on mixed data caused by correlation between classes and diversity in a class are solved; accuracy of classification is improved and classification effect is better.

Description

technical field [0001] The invention relates to a data classification method, in particular to a scalable self-adaptive multi-core classification method, which belongs to the field of artificial intelligence, and specifically belongs to the technical field of data mining. Background technique [0002] Kernel Methods (Kernel Methods) is a widely popular data classification method, which is widely used in many fields. When the data classification task is relatively simple, using a traditional support vector machine (Support Vector Machine, SVM) based on a single kernel function can effectively classify data by learning classifier parameters under the condition of pre-selecting an appropriate kernel function. However, in a data set with a large number of data categories and complex feature distribution, there is a diversity of feature representations between different instances of the same category, and there is a feature correlation between instances of different categories. ...

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is: how to model data categories from different aspects and different granularities in the face of data classification problems with many categories and complex feature representations, so as to solve the problems brought about by inter-category correlation and intra-category diversity. Data aliasing problem, and effectively improve classification accuracy

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