Rule Induction from Decision Tables with Missing Values Based on Variable Precision Rough Sets

Abstract

In this paper, we propose rule induction methods based on rough set theory for decision tables with missing values. Several rule induction methods for decision tables with missing values have been proposed, however, those are based on the knowledge discovery approach. While, the proposed methods are based on the machine learning approach. The proposed methods are based on variable precision rough sets, which can admit error in decision tables. We examine classification accuracy of the proposed methods by numerical experiments.