2022/05/10 by Sandeep Hans, Hans, Sandeep, Diptikalyan Saha +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.2205.04731
openalex publication_date 2022/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.