Multi-classifier integrating method based on increment native Bayes network

Abstract

The invention relates to an increment-based naive Bayesian network multiple classifier integration method, comprising the following steps that: a integration classifier and various key parameters are initialized; if no novel data exists, the process is ended; a category of a novel data item is forecasted by utilization of the prior integration classifier; parameter values of all the individual classifiers are dynamically updated; the weighing of all the individual classifiers is updated; if no error of the category forecast of novel data by the integration classifier is generated, all the individual classifiers in the integration classifier are trained by utilization of the novel data item; redundant individual classifiers are deleted according to the KL pruning strategy; a novel individual classifier is increased; all the individual classifiers are trained by utilization of the novel data item. The increment-based naive Bayesian network multiple classifier integration method can effectively improve the classification forecast result when concept shift is generated, and is particularly suitable for processing the concept shift problem.

Description

technical field [0001] The invention belongs to the field of data mining and machine learning, and relates to an incremental naive Bayesian network-based multi-classifier integration method for conceptual drift data modeling. Background technique [0002] The data in many application fields is constantly increasing, and the patterns contained in it will change with time and application environment, which is called "concept drift". At present, a lot of research has been done on dealing with concept drift at home and abroad, and a variety of model learning methods have been proposed. They can be classified into two categories: methods based on instance selection, such as the FLORA series of algorithms proposed by Widmer and Kubat et al., the algorithm for adaptively adjusting the window size proposed by Lazarescu et al., and the TMF (Time-Windowed Forgetting) algorithm of Salganicoff. The method based on instance selection cannot deal with the problem of concept drift well be...

AI Technical Summary

Problems solved by technology

Existing ensemble-based methods cannot discard useless classifiers in time, causing interference of wrong concepts and affecting classification prediction results

Images