Grey Relational Analysis Based k Nearest Neighbor Missing Data Imputation for Software Quality Datasets

Huang, Jianglin; Sun, Hongyi

doi:10.1109/qrs.2016.20

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…However, this may have resulted in unrealistic assumptions because the dataset used here had no missing values to begin with and the missing values had been imputed at random without taking into account other missing values mechanisms. Another study tested an innovative grey relational analysis method that makes use of the KNN imputation technique to fill in the gaps when dealing with incomplete instances [76]. Research into the effectiveness of the imputation was conducted by applying the method to four datasets with varying artificial missingness configurations.…”

Section: K Nearest Neighbour Classificationmentioning

confidence: 99%

Handling Missing values through Imputation techniques: A Quantitative Survey

Mohit,

Devpriya

2023

Preprint

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This study examines the various techniques used to address missing datain quantitative studies. It acknowledges that missing data poses challengesto data analysis and presents a range of solutions, from simple methodslike deletion and single imputation to advanced approaches involving mod-eling, multiple imputation, and machine learning. The survey takes both abottom-up and top-down approach in selecting an appropriate method forhandling missing data, where the analysis of the data helps narrow down theoptions for the selection process. The study explores the qualitative benefitsand drawbacks of each technique, considering factors such as the analyticalapproach, missing data handling strategy, sample size, and proportion ofmissing data to assess estimation precision. The researchers employed a the-oretical approach using fictitious data to evaluate the significance of missingdata treatment on estimate precision. The statistical analysis conducted inthis study provides practical guidelines for educational researchers, assistingthem in determining the optimal amount of data to collect, the type of anal-ysis to perform, and the appropriate strategy for addressing missing data toensure the reliability of their studies

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Section: K Nearest Neighbour Classificationmentioning

confidence: 99%

Handling Missing values through Imputation techniques: A Quantitative Survey

Mohit,

Devpriya

2023

Preprint

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“…In another research, a novel grey relational analysis approach for incomplete instances using the KNN imputation technique was experimented on [76]. The approach was experimented on four datasets with different artificial missingness set-ups to investigate the performance of the imputation.…”

Section: K Nearest Neighbour Classificationmentioning

confidence: 99%

A Survey On Missing Data in Machine Learning

Emmanuel

Maupong

Mpoeleng

et al. 2021

Preprint

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Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur as a result of various factors like missing completely at random, missing at random or missing not at random. All these may be as a result of system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for. Finally, we experiment on the K nearest neighbor and random forest imputation techniques on novel power plant induced fan data and offer some possible future research direction.

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Missing Data Imputation Techniques for Software Effort Estimation: A Study of Recent Issues and Challenges

AlMutlaq

Jawawi

2019

Advances in Intelligent Systems and Computing

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Grey Relational Analysis Based k Nearest Neighbor Missing Data Imputation for Software Quality Datasets

Cited by 10 publications

References 26 publications

Handling Missing values through Imputation techniques: A Quantitative Survey

Handling Missing values through Imputation techniques: A Quantitative Survey

A Survey On Missing Data in Machine Learning

Missing Data Imputation Techniques for Software Effort Estimation: A Study of Recent Issues and Challenges

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