Fuzzy and rough approach to the problem of missing data in fall detection system

Abstract

Two new methods for mining incomplete data based on interval-valued fuzzy set theory and rough set theory, particularly maximal consistent blocks were proposed, and their application to the fall detection system, exactly to the posture detection system. The suggested methods are based on interval-valued aggregation operators supporting decision-making involving uncertainty. Additionally, the new measure of knowledge was used in two aspects: in the imputation of missing data and during the inference process. The usage of the knowledge measure allowed for a reduction of the uncertainty due to incompleteness and imprecision of data, while increasing the efficiency of the decision-making process, through the additional selection of objects for which the degree of the information measure is the highest. The developed hybrid approach to mining incomplete data and its application in a real decision-making problem yield stable performance despite the increase in missing data.