A Study of a Patent Category Estimation System Using Rough Set Theory

Abstract

In this paper, I propose a patent category estimation system using rough set theory. This system has a rule construction module and an estimation module. A rule construction module tries to decision rules from a decision matrix which is a document term matrix with category labels. It tries rules from lower approximate and upper approximate. An estimation module tries to estimate the category from unlabeled patent using decision rules. According to experiments with an expert, the maximum accuracy ratio is 62%. However, I cannot say this approach is better than the traditional method