Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN

2022 ◽  
Vol 9 (3) ◽  
pp. 0-0
Keyword(s):  
Data Analysis ◽  
Missing Data ◽  
Measurement Errors ◽  
Missing Values ◽  
Missing Value ◽  
Hybrid Schemes ◽  
Imputation Methods ◽  
Research Fields ◽  
Data Missing ◽  
The Given

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

scholarly journals A data-driven missing value imputation approach for longitudinal datasets

2021 ◽  
Author(s):  
Caio Ribeiro ◽  
Alex A. Freitas
Keyword(s):  
Missing Data ◽  
Longitudinal Data ◽  
Missing Values ◽  
Error Rates ◽  
Imputation Method ◽  
Data Driven ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Human Ageing ◽  
Imputation Approach

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.

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jaemun Sim ◽  
Jonathan Sangyun Lee ◽  
Ohbyung Kwon
Keyword(s):  
Data Analysis ◽  
Missing Data ◽  
Real World ◽  
Missing Values ◽  
Complete Data ◽  
Classification Algorithms ◽  
Quality Of Data ◽  
Related Data ◽  
Ubiquitous Environment

In a ubiquitous environment, high-accuracy data analysis is essential because it affects real-world decision-making. However, in the real world, user-related data from information systems are often missing due to users’ concerns about privacy or lack of obligation to provide complete data. This data incompleteness can impair the accuracy of data analysis using classification algorithms, which can degrade the value of the data. Many studies have attempted to overcome these data incompleteness issues and to improve the quality of data analysis using classification algorithms. The performance of classification algorithms may be affected by the characteristics and patterns of the missing data, such as the ratio of missing data to complete data. We perform a concrete causal analysis of differences in performance of classification algorithms based on various factors. The characteristics of missing values, datasets, and imputation methods are examined. We also propose imputation and classification algorithms appropriate to different datasets and circumstances.

scholarly journals Comparison of Algorithms for Clustering Incomplete Data

2014 ◽  
Vol 39 (2) ◽  
pp. 107-127 ◽  
Author(s):  
Artur Matyja ◽  
Krzysztof Siminski
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Missing Values ◽  
Real Data ◽  
Complete Data ◽  
The Other ◽  
Data Sets ◽  
Missing Value ◽  
Comparison Of Algorithms ◽  
New Algorithms

Abstract The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

scholarly journals Application of imputation methods for missing values of PM10 and O3 data: Interpolation, moving average and K-nearest neighbor methods

2021 ◽  
Vol 8 (3) ◽  
pp. 215-226
Author(s):  
Parisa Saeipourdizaj ◽  
Parvin Sarbakhsh ◽  
Akbar Gholampour
Keyword(s):  
Missing Data ◽  
Human Error ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Moving Average ◽  
Classification And Regression Tree ◽  
Coefficient Of Determination ◽  
K Nearest Neighbor ◽  
Imputation Methods ◽  
Machine Failure

Background: PIn air quality studies, it is very often to have missing data due to reasons such as machine failure or human error. The approach used in dealing with such missing data can affect the results of the analysis. The main aim of this study was to review the types of missing mechanism, imputation methods, application of some of them in imputation of missing of PM10 and O3 in Tabriz, and compare their efficiency. Methods: Methods of mean, EM algorithm, regression, classification and regression tree, predictive mean matching (PMM), interpolation, moving average, and K-nearest neighbor (KNN) were used. PMM was investigated by considering the spatial and temporal dependencies in the model. Missing data were randomly simulated with 10, 20, and 30% missing values. The efficiency of methods was compared using coefficient of determination (R2 ), mean absolute error (MAE) and root mean square error (RMSE). Results: Based on the results for all indicators, interpolation, moving average, and KNN had the best performance, respectively. PMM did not perform well with and without spatio-temporal information. Conclusion: Given that the nature of pollution data always depends on next and previous information, methods that their computational nature is based on before and after information indicated better performance than others, so in the case of pollutant data, it is recommended to use these methods.

scholarly journals Treatment of missing data determines conclusions regarding moralizing gods

2019 ◽  
Author(s):  
Bret Beheim ◽  
Quentin Atkinson ◽  
Joseph Bulbulia ◽  
Will M Gervais ◽  
Russell Gray ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Social Complexity ◽  
World History ◽  
Imputation Methods ◽  
Data Points

Whitehouse, et al. have recently used the Seshat archaeo-historical databank to argue that beliefs in moralizing gods appear in world history only after the formation of complex “megasocieties” of around one million people. Inspection of the authors’ data, however, shows that 61% of Seshat data points on moralizing gods are missing values, mostly from smaller populations below one million people, and during the analysis the authors re-coded these data points to signify the absence of moralizing gods beliefs. When we confine the analysis only to the extant data or use various standard imputation methods, the reported finding is reversed: moralizing gods precede increases in social complexity. We suggest that the reported “megasociety threshold” for the emergence of moralizing gods is thus solely a consequence of the decision to re-code nearly two-thirds of Seshat data from unknown values to known absences of moralizing gods.

scholarly journals Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

2017 ◽  
Author(s):  
Runmin Wei ◽  
Jingye Wang ◽  
Mingming Su ◽  
Erik Jia ◽  
Tianlu Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Missing Values ◽  
Pearson Correlation ◽  
Imputation Accuracy ◽  
Metabolomics Data ◽  
Missing Value ◽  
Sample Distribution ◽  
Imputation Methods ◽  
Missing Value Imputation ◽  
Squared Error

AbstractIntroductionMissing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection of methods can significantly affect following data analyses and interpretations. According to the definition, there are three types of missing values, missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).ObjectivesThe aim of this study was to comprehensively compare common imputation methods for different types of missing values using two separate metabolomics data sets (977 and 198 serum samples respectively) to propose a strategy to deal with missing values in metabolomics studies.MethodsImputation methods included zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC). Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate the imputation accuracy for MCAR/MAR and MNAR correspondingly. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes sum of squared error were used to evaluate the overall sample distribution. Student’s t-test followed by Pearson correlation analysis was conducted to evaluate the effect of imputation on univariate statistical analysis.ResultsOur findings demonstrated that RF imputation performed the best for MCAR/MAR and QRILC was the favored one for MNAR.ConclusionCombining with “modified 80% rule”, we proposed a comprehensive strategy and developed a public-accessible web-tool for missing value imputation in metabolomics data.

scholarly journals Missing Data: Current Practice in Football Research and Recommendations for Improvement

2020 ◽  
Author(s):  
David N Borg ◽  
Robert Nguyen ◽  
Nicholas J Tierney
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Current Practice ◽  
Exploratory Analysis ◽  
Imputation Methods ◽  
Research Results ◽  
Study Results

Missing data are often unavoidable. The reason values go missing, along with decisions made of how missing data are handled (deleted or imputed), can have a profound effect on the validity and accuracy of study results. In this article, we aimed to: estimate the proportion of studies in football research that included a missing data statement, highlight several practices to avoid in relation to missing data, and provide recommendations for exploring, visualising and reporting missingness. Football related articles, published in 2019 were studied. A survey of 136 articles, sampled at random, was conducted to determine whether a missing data statement was included. As expected, the proportion of studies in football research that included a missing data statement was low, at only 11.0% (95% CI: 6.3% to 17.5%); suggesting that missingness is seldom considered by researchers. We recommend that researchers describe the number and percentage of missing values, including when there are no missing values. Exploratory analysis should be conducted to explore missing values, and visualisations describing missingness overall should be provided in the paper, or at least supplementary materials. Missing values should almost always be imputed, and imputation methods should be explored to ensure they are appropriately representative. Researchers should consider these recommendations, and pay greater attention to missing data and its influence on research results.

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