一种基于显现模式的懒惰式贝叶斯分类器

A Lazy Bayesian Classification based on essential Emerging Patterns

本文提出了一种新的基于EP的分类算法,即基于基本显现模式的懒惰式贝叶斯分类算法(Lazy Bayesian Classification based on essential Emerging Patterns,LBCeEP),该算法使用懒惰式学习技术进行训练数据集的约简,并使用了一种特殊形式的更能有效地反映类标属性的EP,同时采用贝叶斯方法应用这种EP来进行分类.在UCI机器学习库中的14个数据集上的实验表明,本文所提出的算法具有更好的分类效果。

In this paper, we propose a new classification algorithm called Lazy Bayesian Classification based on essential Emerging Patterns （LBCeEP）. The algorithm applies lazy learning strategy to reduce training set and adopts a special kind of EP for classification. Differing from the existing EP--based classifiers, LBCeEP uses Bayesian approach to measure the contribution of EP for classification. On the 14 data sets singled out from the UCI machine learning repository, the experimental results have shown that LBCeEP algorithm can produce good classification results.