A novel k-NN approach for data with uncertain attribute values

Abstract

Data uncertainty arises in several real world domains, including machine learning and pattern recognition applications. In classification problems, we could very well wind up with uncertain attribute values that are caused by sensor failures, measurements approximations or even subjective expert assessments, etc. Despite their seriousness, these kinds of data are not well covered till now. In this paper, we propose to develop a machine learning model for handling such kinds of imperfection. More precisely, we suggest to develop a new version of the well known k-nearest neighbors classifier to handle the uncertainty that occurs in the attribute values within the belief function framework.