论文部分内容阅读
In the pattern recognition field, error-correcting output codes(ECOC) are a powerful tool to fuse any number of binary classifiers to model multiclass problems, and the research of encoding based on data is attracting more and more attention. In this paper, we are going to propose a new encoding method for constructing subclass Error-Correcting Output Codes, which was first introduced by Escalera et al. To achieve this goal, we first obtain the correlation between each pair of classes with the help of confusion matrix. Then,we select the most easily separated subclasses for classification by following Fisher’s principle. At last, we were able to obtain binary partitions based on subclasses. After finishing this work, a new data-driven coding matrixSubclass ECOC will be achieved. Experimental results on University of California Irvine data sets and three kinds of high resolution range profile data sets with logistic linear classifier and support vector machine as the binary classifiers show that our approach can provide a better performance and the robustness of classification with a little longer but acceptable code length.
In the pattern recognition field, error-correcting output codes (ECOC) are a powerful tool to fuse any number of binary classifiers to model multiclass problems, and the research of encoding based on data is attracting more and more attention. In this paper, we are going to propose a new encoding method for constructing subclass Error-Correcting Output Codes, which was first introduced by Escalera et al. To achieve this goal, we first obtain the correlation between each pair of classes with the help of confusion matrix. Then, we select the most Eas separated subclasses for classification by the following Fisher’s principle. At last, we were able to obtain binary partitions based on subclasses. After finishing this work, a new data-driven coding matrix Subclass ECOC will be achieved. Experimental results on University of California Irvine data sets and three kinds of high resolution range profile data sets with logistic linear classifier and support vector machine as the binary classifiers show that our approach can provide a better performance and the robustness of classification with a little longer but acceptable code length.