Experimental analysis of methods for imputation of missing values in databases

Alireza Farhangfar; Lukasz A. Kurgan; Witold Pedrycz

doi:10.1117/12.542509

12 April 2004 Experimental analysis of methods for imputation of missing values in databases

Alireza Farhangfar, Lukasz A. Kurgan, Witold Pedrycz

Proceedings Volume 5421, Intelligent Computing: Theory and Applications II; (2004) https://doi.org/10.1117/12.542509
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

A very important issue faced by researchers and practitioners who use industrial and research databases is incompleteness of data, usually in terms of missing or erroneous values. While some of data analysis algorithms can work with incomplete data, a large portion of them require complete data. Therefore, different strategies, such as deletion of incomplete examples, and imputation (filling) of missing values through variety of statistical and machine learning (ML) procedures, are developed to preprocess the incomplete data. This study concentrates on performing experimental analysis of several algorithms for imputation of missing values, which range from simple statistical algorithms like mean and hot deck imputation to imputation algorithms that work based on application of inductive ML algorithms. Three major families of ML algorithms, such as probabilistic algorithms (e.g. Naive Bayes), decision tree algorithms (e.g. C4.5), and decision rule algorithms (e.g. CLIP4), are used to implement the ML based imputation algorithms. The analysis is carried out using a comprehensive range of databases, for which missing values were introduced randomly. The goal of this paper is to provide general guidelines on selection of suitable data imputation algorithms based on characteristics of the data. The guidelines are developed by performing a comprehensive experimental comparison of performance of different data imputation algorithms.

Citation Download Citation

Alireza Farhangfar, Lukasz A. Kurgan, and Witold Pedrycz "Experimental analysis of methods for imputation of missing values in databases", Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); https://doi.org/10.1117/12.542509

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available