Explainable Data Imputation using Constraints

TL;DR

This paper introduces a novel explainable data imputation algorithm that leverages attribute dependencies and constraints, providing both accurate imputations and human-readable explanations, improving data analysis reliability.

Contribution

The paper presents a new data imputation method that considers attribute dependencies and generates explanations, addressing gaps in existing approaches.

Findings

Outperforms state-of-the-art imputation techniques on various metrics.

Provides human-readable explanations for each imputed value.

Handles different data types and attribute constraints effectively.

Abstract

Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposition, require complete data. Many approaches impute numeric data and some do not consider dependency of attributes on other attributes, while some require human intervention and domain knowledge. We present a new algorithm for data imputation based on different data type values and their association constraints in data, which are not handled currently by any system. We show experimental results using different metrics comparing our algorithm with state of the art imputation techniques. Our algorithm not only imputes the missing values but also generates human readable explanations describing the significance of attributes used for every imputation.