Hybrid Missing Value Imputation Algorithms Using Fuzzy C-Means and Vaguely Quantified Rough Set

Author(s):  
Daiwei Li ◽  
Haiqing Zhang ◽  
Tianrui Li ◽  
Abdelaziz Bouras ◽  
Xi Yu ◽  
...  
2018 ◽  
Author(s):  
Stefan Bischof ◽  
Andreas Harth ◽  
Benedikt KKmpgen ◽  
Axel Polleres ◽  
Patrik Schneider

Author(s):  
Caio Ribeiro ◽  
Alex A. Freitas

AbstractLongitudinal datasets of human ageing studies usually have a high volume of missing data, and one way to handle missing values in a dataset is to replace them with estimations. However, there are many methods to estimate missing values, and no single method is the best for all datasets. In this article, we propose a data-driven missing value imputation approach that performs a feature-wise selection of the best imputation method, using known information in the dataset to rank the five methods we selected, based on their estimation error rates. We evaluated the proposed approach in two sets of experiments: a classifier-independent scenario, where we compared the applicabilities and error rates of each imputation method; and a classifier-dependent scenario, where we compared the predictive accuracy of Random Forest classifiers generated with datasets prepared using each imputation method and a baseline approach of doing no imputation (letting the classification algorithm handle the missing values internally). Based on our results from both sets of experiments, we concluded that the proposed data-driven missing value imputation approach generally resulted in models with more accurate estimations for missing data and better performing classifiers, in longitudinal datasets of human ageing. We also observed that imputation methods devised specifically for longitudinal data had very accurate estimations. This reinforces the idea that using the temporal information intrinsic to longitudinal data is a worthwhile endeavour for machine learning applications, and that can be achieved through the proposed data-driven approach.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tressy Thomas ◽  
Enayat Rajabi

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.


2021 ◽  
Vol 4 (8) ◽  
pp. 20-31
Author(s):  
Agnes Tuti Rumiati ◽  
◽  
Nur Achmey Selgi Harwanti ◽  
Muhammad Rif’an ◽  
Haniza Annuril Chusna ◽  
...  

The National Education Standard is one of the government's efforts to achieve equitable quality education. National Education Standards include eight outcomes, namely Graduation Competency Standards, Content Standards, Process Standards, Assessment Standards, Educators and Educators Standards, Facilities and Infrastructure Standards, Management Standards, and Financing Standards. This research was conducted to classify elementary and junior high schools in Indonesia based on SNP using the Fuzzy C-Means method. Prior to the cluster analysis, the missing value imputation was carried out using regression. The variables that have the lowest median and average value are the standard variables of educators and education personnel, while those with the highest value are the process standards. Based on the results of grouping using C-Means, the optimum number of clusters is four clusters with the most members being cluster 1 (the best cluster). Keywords: Education; Clustering; Fuzzy C-Means; imputation missing value


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