摘要
朴素贝叶斯分类器难以获得大量有类标签的训练集,而且传统的贝叶斯分类方法在有新的训练样本加入时,需要重新学习已学习过的样本,耗费大量时间。为此引入增量学习方法,在此基础上提出了属性加权朴素贝叶斯算法,该算法通过属性加权来提高朴素贝叶斯分类器的性能,加权参数直接从训练数据中学习得到。通过由W eka推荐的UC I数据集的实验结果表明,该算法是可行的和有效的。
Naive Bayesian classifiers have difficult problems involving getting labeled training datasets, and cost a lot of time to learn all samples again when new sample adds. Motivated by this fact, the paper presents an incremental learning method, and proposes a weighted naive Bayesian classification algorithm. All of them improve the performance of naive Bayesian classifiers at the expense of attribute weights, the attribute weighted parameters are directly induced from training dataset. Experimentally testing the algorithm using the UCI datasets recommended by Weka, the results show that the algorithm is feasible and effective.
出处
《计算机与现代化》
2010年第5期30-32,共3页
Computer and Modernization
关键词
朴素贝叶斯分类器
属性加权
增量学习
训练集
naive Bayesian classifiers
attribute weights
incremental learning
training dataset
