摘要
依据copula和贝叶斯网络理论,将高斯copula函数、引入平滑参数的高斯核函数和以分类器的分类准确性为标准的属性父结点贪婪选择等相结合,综合考虑效率和可靠性,进行连续属性一阶贝叶斯衍生分类器学习、优化和集成.使用UCI数据库中连续属性分类数据进行实验,结果显示,经过优化和集成的一阶连续属性贝叶斯衍生分类器具有良好的分类准确性.
Considering the efficiency and reliablity, Gaussian copula function, Gaussian kernel function with smoothing parameter, the criteria of classification accuracy and the greedy selection of attribute parent node are combined to carry out the learning, optimization and ensemble of one-order Bayesian derivative classifiers with continuous attributes. Experiment and analysis are executed by using datasets with continuous attributes in UCI dataset. The results show that the classifiers after optimization and ensemble have good classification accuracy.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2015年第6期499-506,共8页
Pattern Recognition and Artificial Intelligence
基金
上海市自然科学基金项目(No.15ZR1429700)
上海市教委科研创新项目(No.15ZZ099)资助