Bayesian Classification Based on Simulated Annealing Genetic Algorithms
Cheng Zhuan-liu · Jisuanji gongcheng · 2007
Although Nave Bayesian classifier is a simple and highly efficient classification method,its attribute of independence assumption limits its real application.A new algorithm is introduced in this paper to avoid the direct influence of feature reduction on the performance of classification.This algorithm generates certain attribute subsets of the training sets through random attribute selection,constructs the corresponding Nave Bayesian classifiers,and optimizes the Bayesian classifiers by using simulated annealing genetic algorithms.Experiment shows that this algorithm has better performance when compared with traditional Nave Bayesian methods.