research-article

Investigations into Missing Values Imputation Using Random Forests for Semi-supervised Data

Author:

Tsunenori IshiokaAuthors Info & Claims

iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services

Pages 296 - 301

https://doi.org/10.1145/2684200.2684288

Published: 04 December 2014 Publication History

Abstract

This paper presents a revised procedure that imputes missing values by using random forests on semi-supervised data. The method has a feature that not only allows missing data to be found in a response variable but in a predictive variable, and furthermore, it can now deal with any types of data, i.e., numerical values, categories and categories with an order. By evaluating this method using Titanic data and eleven UC Irvine repository datasets, we found that our method performed fairly well, and a method of naive median imputation was also suitable in these cases.

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Cited By

Muller MStrohmayer A(2022)Forgetting Practices in the Data SciencesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517644(1-19)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517644
Adomavicius GBockstedt JCurley SZhang J(2022)Recommender systems, ground truth, and preference pollutionAI Magazine10.1002/aaai.1205543:2(177-189)Online publication date: 23-Jun-2022
https://dl.acm.org/doi/10.1002/aaai.12055
Faucheux LSoumelis VChevret S(2021)Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation frameworkBiometrical Journal10.1002/bimj.20200036564:8(1446-1466)Online publication date: 27-Jun-2021
https://doi.org/10.1002/bimj.202000365

Index Terms

Investigations into Missing Values Imputation Using Random Forests for Semi-supervised Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Instance-based learning
2. Mathematics of computing
  1. Mathematical software

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iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services

December 2014

587 pages

ISBN:9781450330015

DOI:10.1145/2684200

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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@WAS: International Organization of Information Integration and Web-based Applications and Services
Johannes Kepler Univ Linz: Johannes Kepler Universität Linz

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2014

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Grant-in-Aid for Scientic Research

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iiWAS '14

iiWAS '14: The 16th International Conference on Information Integration and Web-based Applications & Services

December 4 - 6, 2014

Hanoi, Viet Nam

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Cited By

Muller MStrohmayer A(2022)Forgetting Practices in the Data SciencesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517644(1-19)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517644
Adomavicius GBockstedt JCurley SZhang J(2022)Recommender systems, ground truth, and preference pollutionAI Magazine10.1002/aaai.1205543:2(177-189)Online publication date: 23-Jun-2022
https://dl.acm.org/doi/10.1002/aaai.12055
Faucheux LSoumelis VChevret S(2021)Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation frameworkBiometrical Journal10.1002/bimj.20200036564:8(1446-1466)Online publication date: 27-Jun-2021
https://doi.org/10.1002/bimj.202000365

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