article

Incomplete-case nearest neighbor imputation in software measurement data

Authors:

Jason Van Hulse,

Taghi M. KhoshgoftaarAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 259

Pages 596 - 610

https://doi.org/10.1016/j.ins.2010.12.017

Published: 01 February 2014 Publication History

Abstract

k nearest neighbor imputation (kNNI) is one of the most popular methods in empirical software engineering for imputing missing values. kNNI typically uses only complete cases as possible donors for imputation (called complete case kNNI or CCkNNI). Though it often produces reasonable results, CCkNNI is severely limited when the amount of missing data is large (and hence the number of complete cases is small). In response, a variant of CCkNNI called incomplete case k nearest neighbor imputation (ICkNNI) has been proposed as an attractive alternative. This work presents a detailed simulation comparing CCkNNI and ICkNNI using two different software measurement datasets. The empirical results show that using incomplete cases often increases the effectiveness of nearest neighbor imputation (especially at higher missingness levels), regardless of the type of missingness (i.e., the distribution of missing values in the data).

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Digital Library

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J. Van Hulse, T.M. Khoshgoftaar, Incomplete-case nearest neighbor imputation in software measurement data, in: Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI 2007), Las Vegas, NV, 2007, pp. 630-637.

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Cited By

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 259, Issue

February, 2014

611 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2011.

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Elsevier Science Inc.

United States

Publication History

Published: 01 February 2014

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Cited By

Matias AGomes JMattos CRocha Neto AMesquita D(2024)Bayesian ART for incomplete datasetsApplied Soft Computing10.1016/j.asoc.2024.111865163:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111865
He D(2023)Active learning for ordinal classification on incomplete dataIntelligent Data Analysis10.3233/IDA-22666427:3(613-634)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/IDA-226664
Abnane IIdri AChlioui IAbran A(2023)Evaluating ensemble imputation in software effort estimationEmpirical Software Engineering10.1007/s10664-022-10260-028:2Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1007/s10664-022-10260-0
Abnane IIdri AAbran A(2023)Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimationJournal of Software: Evolution and Process10.1002/smr.252936:4Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1002/smr.2529
Yu KYang YDing W(2022)Causal Feature Selection with Missing DataACM Transactions on Knowledge Discovery from Data10.1145/348805516:4(1-24)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3488055
Nugroho HUtama NSurendro K(2020)Performance Evaluation for Class Center-Based Missing Data Imputation AlgorithmProceedings of the 2020 9th International Conference on Software and Computer Applications10.1145/3384544.3384575(36-40)Online publication date: 18-Feb-2020
https://dl.acm.org/doi/10.1145/3384544.3384575
Hamidzadeh JMoradi M(2020)Enhancing data analysis: uncertainty-resistance method for handling incomplete dataApplied Intelligence10.1007/s10489-019-01514-450:1(74-86)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10489-019-01514-4
Lin WTsai C(2020)Missing value imputation: a review and analysis of the literature (2006–2017)Artificial Intelligence Review10.1007/s10462-019-09709-453:2(1487-1509)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10462-019-09709-4
Abnane IIdri AAbran A(2020)Fuzzy case‐based‐reasoning‐based imputation for incomplete data in software engineering repositoriesJournal of Software: Evolution and Process10.1002/smr.226032:9Online publication date: 3-Sep-2020
https://dl.acm.org/doi/10.1002/smr.2260
Bosu MMacdonell S(2019)ExperienceJournal of Data and Information Quality10.1145/332874611:4(1-38)Online publication date: 19-Aug-2019
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