Annotation-based Document Classification Using Shuffled Frog Leaping Algorithm

The number of online documents has grown greatly in recent years due to the increase in popularity of World Wide Web (WWW). The main task of assigning a document of corpus to a set of previously fixed categories is known as document classification. The main issues of document classification involve extraction of discriminating features and then classification of documents based on these features. The accuracy of classification can be improved by considering semantic relation between documents. The proposed work uses annotation retrieval wisdom to improve the accuracy of the document retrieval based on semantic relatedness. Shuffled frog leaping (SFL) algorithm promotes the idea of efficient document classification. Annotation uses singular value decomposition (SVD) which helps to obtain a semantic relationship among documents. This facilitates the accurate retrieval of knowledge between individual and various classes of documents.