Date of Award


Publication Type


Degree Name



Electrical and Computer Engineering

First Advisor

M. Ahmadi

Second Advisor

S. Alirezaee

Third Advisor

M. Hassanzadeh


Data imputation, Differential dependency, Fuzzy multi-objective programming, Integer linear programming, IZM algorithm, Missing data




Missing or incomplete data is a serious problem when it comes to collecting and analyzing data for forecasting, estimating, and decision making. Since data quality is so important in machine learning and its results, in most cases data imputation is much more appropriate than ignoring them. Missing data imputation is often based on considering equality, similarity, or distance of neighbors. Researchers use different approaches for neighbors' equalities or similarities. Every approach has its advantages and limitations. Instead of equality, some researchers use inequalities together with a few relationships or similarity rules. In this thesis, after recalling some basic imputation methods, we discus about data imputation based on differential dependencies (DDs). DDs are conditional rules in which the closeness of the values of each pair of tuples in some attribute indicates the closeness of the values of those tuples in another attribute. Considering these rules, a few rows are created for each incomplete row and placed in the set of candidates for that row. Then from each set one row is selected such that they are not incompatible with each other. These selections are made by an integer linear programming (ILP) model. In this thesis, first, we propose an algorithm to generate DDs. Then in order to improve the previous approaches to increase the percentage of imputation, we suggest fuzzy relaxation that allows a little violation from DDs. Finally, we propose a multi-objective fuzzy linear programming to reach an imputation with more percentage of imputation in addition to decrease the summation of violations. A variety of datasets from “Kaggle” is used to support our approach.