DECISION COMBINATION IN MULTIPLE CLASSIFIER SYSTEMS
DECISION COMBINATION IN MULTIPLE CLASSIFIER SYSTEMS
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DOI：
10.1109/34.273716
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发表时间：
1994-01-01
影响因子：
23.6
通讯作者：
SRIHARI, SN
中科院分区：
文献类型：
--
作者：
HO, TK;HULL, JJ;SRIHARI, SN
多个分类器系统是涉及大型类集和嘈杂输入的困难模式识别问题的有力解决方案，因为它允许同时使用任意特征描述符和分类过程。分类器的决策可以表示为类的排名，以便它们在不同类型的分类器和问题的不同实例之间具有可比性。排名可以通过减少或重读给定的一组类的方法来组合。提出了一种交点方法和联合方法，以减少班级。提出了基于最高等级的三种方法，即Borda计数和logistic回归用于类集的重读。这些方法已在降级的机器打印字符和大型词典的单词的应用中进行了测试，从而大大提高了整体正确性。
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classes so that they are comparable across different types of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and a union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications on degraded machine-printed characters and words from large lexicons, resulting in substantial improvement in overall correctness.