Not so greedy: Randomly Selected Naive Bayes
Not so greedy: Randomly Selected Naive Bayes
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不那么贪婪：随机选择朴素贝叶斯
DOI：
10.1016/j.eswa.2012.03.022
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发表时间：
2012-09-15
影响因子：
8.5
通讯作者：
Wang, Dianhong
中科院分区：
文献类型：
--
作者：
Jiang, Liangxiao;Cai, Zhihua;Wang, Dianhong
人们提出了许多方法来改进朴素贝叶斯，其中属性选择方法表现出了显著的性能。属性选择算法分为两大类：过滤器和包装器。过滤器使用一般的数据特征来评估选择的属性子集之前，学习算法运行，而包装器使用学习算法本身作为一个黑盒来评估选择的属性子集。本文对包装器的属性选择方法进行了研究，提出了一种改进的朴素贝叶斯算法，通过在整个属性空间中进行随机搜索来选择包装器的属性。我们将其称为随机选择朴素贝叶斯（RSNB）。为了满足分类、排序和类概率估计的需要，我们区分性地设计了三个不同的版本：RSNB-ACC、RSNB-AUC和RSNB-CLL。基于大量UCI数据集的实验结果分别从分类准确率（ACC）、ROC曲线下面积（AUC）和条件对数似然（CLL）三个方面验证了它们的有效性。(C)2012爱思唯尔有限公司保留所有权利。
Many approaches are proposed to improve Naive Bayes, among which the attribute selection approach has demonstrated remarkable performance. Algorithms for attribute selection fall into two broad categories: filters and wrappers. Filters use the general data characteristics to evaluate the selected attribute subset before the learning algorithm is run, while wrappers use the learning algorithm itself as a black box to evaluate the selected attribute subset. In this paper, we work on the attribute selection approach of wrapper and propose an improved Naive Bayes algorithm by carrying a random search through the whole space of attributes. We simply called it Randomly Selected Naive Bayes (RSNB). In order to meet the need of classification, ranking, and class probability estimation, we discriminatively design three different versions: RSNB-ACC, RSNB-AUC, and RSNB-CLL. The experimental results based on a large number of UCI datasets validate their effectiveness in terms of classification accuracy (ACC), area under the ROC curve (AUC), and conditional log likelihood (CLL), respectively. (C) 2012 Elsevier Ltd. All rights reserved.