一个用于三个类的中性区域分类器,用于文本挖掘

Dylan C. Friel, Yunzhe Li, Benjamin Ellis, D. Jeske, Herbert K. H. Lee, P. Kass
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引用次数: 0

摘要

分类器可能受到其条件误分类率的限制,而不是其总体误分类率。在一个或多个条件误分类率很高的情况下,可以引入中性区来降低并可能平衡误分类率。本文在三类分类器中引入一个中性区,通过控制条件误分类率来确定其区域。中性区分类器用一个文本挖掘应用程序来说明,该应用程序对与学生教学评估相关的书面评论进行分类。
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A neutral zone classifier for three classes with an application to text mining
A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.
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