Studies on Cover Algorithm of Classification Decision
分类判别的覆盖算法研究

Data-based machine learning is exploring the rule to predict new data from the observation data. In this paper, a novel classification decision method, called as Cover Algorithm (CA), is presented. In the training procedure, some representative samples of the training set can be obtained by utilizing a certain cover rule. Then, in the classification phase, the classifier can make a decision according to the distances from a test sample to the representatives, namely the class of the test sample is determined by the representative closest to the test sample. Comparing with the nearest neighbor method, the presented method needs less cost and memory space as the representative samples are only a little part of the training set. Furthermore, cover algorithm is suitable for automated classification of large data because it does not need to consider choosing kernel function like SVM and its main computation is distance operation between samples. The experiment results show that the cover algorithm has good robustness and high classifying accuracy over Normal Galaxies and stars datasets.