Intelligent information retrieval using rough set approximations

摘要

The theory of rough sets was introduced in 1982. It allows us to classify objects into sets of equivalent members based on their attributes. We may then examine any combination of the same objects (or even their attributes) using the resultant classification. The theory has direct applications in the design and evaluation of classification schemes and the selection of discriminating attributes. Introductory papers discuss its application in the domain of medical diagnostic systems. Here we apply it to the design of information retrieval systems accessing collections of documents. Advantages offered by the theory are: the implicit inclusion of Boolean logic; term weighting; and the ability to rank retrieved documents. In the first section, we describe the theory. This is derived from the work by others in the field and includes only the most relevant aspects of the theory. In the second, we apply it to information retrieval. Specifically, we design the approximation space, search strategies, and illustrate the application of relevance feedback to improve document indexing. In Section 3, we compare the rough set formalism to the Boolean, Vector, and Fuzzy models of information retrieval. Finally, we present a small-scale evaluation of rough sets that indicates its potential in information retrieval.