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基于模拟退火遗传算法的贝叶斯分类
引用本文:胡为成,程转流,王本年.基于模拟退火遗传算法的贝叶斯分类[J].计算机工程,2007,33(9):219-221.
作者姓名:胡为成  程转流  王本年
作者单位:[1]铜陵学院计算机系,铜陵244000 [2]南京大学计算机学院,南京240000
基金项目:安徽省自然科学基金 , 安徽省高等学校自然科学基金重点项目
摘    要:朴素贝叶斯分类器是一种简单而高效的分类器,但是其属性独立性假设限制了对实际数据的应用。文章提出一种新的算法,该算法为避免数据预处理时的属性约简对分类效果的直接影响,在训练集上通过随机属性选取生成若干属性子集,以这些子集构建相应的朴素贝叶斯分类器,采用模拟退火遗传算法进行优选。实验表明,与传统的朴素贝叶斯方法相比,该方法具有更好的性能。

关 键 词:数据挖掘  朴素贝叶斯  模拟退火算法  遗传算法  属性约简  适应度函数
文章编号:1000-3428(2007)09-0219-03
修稿时间:2006-07-10

Bayesian Classification Based on Simulated Annealing Genetic Algorithms
HU Weicheng,CHENG Zhuanliu,WANG Bennian.Bayesian Classification Based on Simulated Annealing Genetic Algorithms[J].Computer Engineering,2007,33(9):219-221.
Authors:HU Weicheng  CHENG Zhuanliu  WANG Bennian
Affiliation:1. Department of Computer, Tongling College, Tongling 244000; 2. College of Computer, Nanjing University, Nanjing 240000
Abstract:Although Naïve 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 Naïve 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 Naïve Bayesian methods.
Keywords:Data mining  Naï  ve Bayesian  Simulated annealing algorithms  Genetic algorithms  Feature reduction
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