Open AccessDealing with categorical missing data using CleanerR
Author(s) -
Rafael Pereira,
Fábio Porto
Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/bresci.2019.10032
Subject(s) - missing data , categorical variable , multitude , computer science , data mining , data integration , data science , machine learning , philosophy , epistemology
Missing data is a common problem in the world of data analysis. They appear in datasets due to a multitude of reasons, from data integration to poor data input. When faced with the problem, the analyst must decide what to do with the missing data since its not always advisable to discard these values from your analysis. On this paper we shall discuss a method that takes into account information theory and functional dependencies to best imput missing values.