The research of the classification problem about machine learning based on granularity: calculating and covering algorithm

This paper puts forward the use of granularity calculating of quotient space theory dealing with classification problems about machine learning. According to the prior knowledge or the clustering of the training examples, training data are reorganized with granularity to form new instances, and to learn from the new instances. The different sorts of instances, which are combined in granularity, are classified through different layers of classifiers. In this way, the difficulty of the learning is reduced, the capacity of learning from instances is increased, and the classifying accuracy is improved. At the same time, the method can recognize the different sorts of instances, which features are very similar, and improve its generalization of recognition, and reduce the complicacy of calculation. The detailed procedures of the method using covering algorithm and its experimental results are presented. The results show that the method is effective.