کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
387696 | 660906 | 2012 | 7 صفحه PDF | دانلود رایگان |

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.
► Attribute selection is an important approach for improving Naive Bayes.
► We propose an improved Naive Bayes algorithm with random attribute selection.
► We discriminatively design three different versions for different learning goals.
► The experiments on 36 UCI datasets validate their effectiveness.
Journal: Expert Systems with Applications - Volume 39, Issue 12, 15 September 2012, Pages 11022–11028