摘要
A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.
针对文档分类过程中不同大小文档表示、文档特征选择和文档特征编码问题,提出了一种基于粗糙集的角分类神经网络Rough-CC4.利用近义词构成等价类,以此表示文档,可以缩小文档表示的维数、解决由于文档不同大小导致的精度问题、模糊近义词之间的差别;利用二进制编码方法对文档特征编码,可以提高Rough-CC4的精度,同时减小Rough-CC4的空间复杂度.Rough-CC4可以广泛用于大量文档集合的自动分类.
基金
The National Natural Science Foundation of China(No.60503020,60373066,60403016,60425206),the Natural Science Foundation of Jiangsu Higher Education Institutions ( No.04KJB520096),the Doctoral Foundation of Nanjing University of Posts and Telecommunication (No.0302).
