An effective evidence theory based k-nearest neighbor (knn) classification

Abstract In this paper, we study various K nearest neighbor (KNN

Change Detection in XML Documents for Fixed Structures using Exclusive-Or (XOR)

Abstract XML is an emerging standard for the representation and exchange of Internet data. The characteristics of XML, tree-structured (i.e. a collection of nodes) and self-descriptive, facilitate the detection of changes in an XML document in minute detail and at a finer grain than obtainable at the document level. Furthermore, for a fixed schema (structure) changes may frequently happen in the content or data values of XML documents in the Web. We wish to propose a method that effectively detects this content or data value changes. Rather than inspecting all nodes between two versions of XML documents, we propose an effective algorithm, called top-down, which will detect changes in XML documents by exploring a subset of nodes in the tree. We would like to be certain that if a leaf node changes the algorithm will detect these changes, not only by inspecting the node itself, but also its parent node, grand parent node, and so on. For this, a signature for each node will be constructed which is basically an abstraction of the information stored in a node. There are several ways we can construct such a signature. We will choose exclusive-or (XOR) to construct node signatures which will prevent a user from getting irrelevant information/change and make certain that the user does not miss relevant information. Note that for the web, along with being able to access huge quantities of information, the relevancy of information/change is more important than missing of relevant information/change. For this, in this paper we propose an automatic change detection algorithm which will identify changes between two versions of an XML document based on these signatures using XOR. Our proposed algorithm will traverse the least number of nodes necessary to detect these changes. We demonstrate that our algorithm outperforms the traditional algorithm which exhaustively searches the entire space. We will also demonstrate analytically and empirically that the miss of relevant change is within tolerable range

An effective evidence theory based k-nearest neighbor (knn) classification

Abstract In this paper, we study various K nearest neighbor (KNN