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Imputation of missing values for semi-supervised data using the proximity in random forests

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IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services

Pages 319 - 322

https://doi.org/10.1145/2428736.2428793

Published: 03 December 2012 Publication History

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Abstract

This paper presents a procedure that imputes missing values by using random forests on semi-supervised data. We found that the rate of correct classification of our method is higher than that of other methods: a simple expansion of Liaw's "rfImpute" for (un)supervised data and the k-nearest neighbor method (kNN). Our method can handle missing predictor variables as well as missing response variable. An imputation that uses random forests for semi-supervised cases in the training data set has never been implemented until now.

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Cited By

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Wilson MDatta RSavarimuthu SMoller DRuf C(2024)Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning ModelIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS53475.2024.10642723(4465-4471)Online publication date: 7-Jul-2024
https://doi.org/10.1109/IGARSS53475.2024.10642723
Bzai JAlam FDhafer ABojović MAltowaijri SNiazi IMehmood R(2022)Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry PerspectiveElectronics10.3390/electronics1117267611:17(2676)Online publication date: 26-Aug-2022
https://doi.org/10.3390/electronics11172676
Fan NZhu AQin CLiang P(2020)Digital Soil Mapping over Large Areas with Invalid Environmental Covariate DataISPRS International Journal of Geo-Information10.3390/ijgi90201029:2(102)Online publication date: 6-Feb-2020
https://doi.org/10.3390/ijgi9020102

Index Terms

Imputation of missing values for semi-supervised data using the proximity in random forests
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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Published In

IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services

December 2012

432 pages

ISBN:9781450313063

DOI:10.1145/2428736

General Chair:
Eric Pardede
La Trobe University, Australia

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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Published: 03 December 2012

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IIWAS '12: The 14th International Conference on Information Integration and Web-based Applications & Services

December 3 - 5, 2012

Bali, Indonesia

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Wilson MDatta RSavarimuthu SMoller DRuf C(2024)Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning ModelIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS53475.2024.10642723(4465-4471)Online publication date: 7-Jul-2024
https://doi.org/10.1109/IGARSS53475.2024.10642723
Bzai JAlam FDhafer ABojović MAltowaijri SNiazi IMehmood R(2022)Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry PerspectiveElectronics10.3390/electronics1117267611:17(2676)Online publication date: 26-Aug-2022
https://doi.org/10.3390/electronics11172676
Fan NZhu AQin CLiang P(2020)Digital Soil Mapping over Large Areas with Invalid Environmental Covariate DataISPRS International Journal of Geo-Information10.3390/ijgi90201029:2(102)Online publication date: 6-Feb-2020
https://doi.org/10.3390/ijgi9020102
Ishioka T(2014)Investigations into Missing Values Imputation Using Random Forests for Semi-supervised DataProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684288(296-301)Online publication date: 4-Dec-2014
https://dl.acm.org/doi/10.1145/2684200.2684288

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

Missing Value Imputation Method Using Separate Features Nearest Neighbors Algorithm

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

Missing Value Imputation Method Using Separate Features Nearest Neighbors Algorithm

A new imputation method for small software project data sets

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