一种局部属性加权朴素贝叶斯分类算法

朴素贝叶斯模型具有的简单性和有效性,使其在诸多问题领域表现出优良的性能,但其属性条件独立性假设在实际应用中难以成立.而属性加权是降低属性条件独立性假设对分类器性能影响的主要途径.传统建立在整个数据集上的单一全局模型忽略了每个测试实例所具有的特点,同时从整个训练集上学习到的属性权重并不能准确反映每个属性对待分类实例的影响.为此提出一种基于数据驱动的懒惰式局部属性加权方法,它在每个测试实例的近邻集合上学习属性权重,并通过最优化方法建立相应的局部属性加权朴素贝叶斯模型.实验结果表明:和当前常见的准朴素贝叶斯模型相比,本文模型具有较高的分类准确率.

A locally attribute weighted naive Bayes classifier

Naive Bayes(NB) classifier has exhibited excellent performance on many problem domains due to its simplicity and efficiency.In reality the conditional independence assumption of Naive Bayes isn't always true.Attribute weighting is one of the most popular methods to alleviate this assumption's influence on classification results.However,traditional classification models ignore characteristics of each test instance,and the weight vector learned from the whole training set failed to reflect each attribute's contribution of distinguishing each test instance correctly.To this end,a data driven lazy learning locally attribute weighted naive Bayes model is proposed.The attribute weights for each test instance are learned from its neighborhoods,and learned weights are employed to build the locally weighted model by optimization method.Experimental results on benchmark datasets demonstrate that the proposed approach is more accurate than other classical classifiers.