METHODS OF COMBINING MULTIPLE CLASSIFIERS AND THEIR APPLICATIONS TO HANDWRITING RECOGNITION
METHODS OF COMBINING MULTIPLE CLASSIFIERS AND THEIR APPLICATIONS TO HANDWRITING RECOGNITION
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DOI：
10.1109/21.155943
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发表时间：
1992-05-01
期刊：
影响因子：
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通讯作者：
SUEN, CY
中科院分区：
文献类型：
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作者：
XU, L;KRZYZAK, A;SUEN, CY
将多个分类器的分类能力结合起来的方法被认为是模式识别各个应用领域中的一个普遍问题，并进行了系统的研究。根据各种分类器提供的信息水平，可将该问题的可能解决办法分为三类。基于不同的方法，提出了四种解决这一问题的方法。一种适合于组合贝叶斯、k-NN和各种距离分类器等单独的分类器。另外三个可以用来组合任何类型的个体量词。将这些方法应用于组合多个分类器对完全无约束手写数字的识别，实验结果表明，单个分类器的性能可以得到显著提高。例如，在美国邮政编码数据库上，可以获得98.9%的识别结果，0.90%的替换和0.2%的拒绝，以及95%的识别，0%的替换和5%的拒绝的高可靠性。与欧洲、亚洲和北美的其他研究小组相比，这些结果更具优势。
Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research groups in Europe, Asia, and North America.