scholarly journals Advanced methods for missing values imputation based on similarity learning

2021 ◽  
Vol 7 ◽  
pp. e619
Author(s):  
Khaled M. Fouad ◽  
Mahmoud M. Ismail ◽  
Ahmad Taher Azar ◽  
Mona M. Arafa
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Imputation Accuracy ◽  
Nearest Neighbors ◽  
Imputation Method ◽  
Data Imputation ◽  
K Nearest Neighbors ◽  
Missing Data Imputation ◽  
K Value ◽  
Imputation Methods

The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.

scholarly journals Missing Data Imputation Method for Autism Prediction

2020 ◽  
Vol 8 (5) ◽  
pp. 940-944
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Missing Values ◽  
Imputation Method ◽  
Support Vector ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
Significant Difference ◽  
Friedman's Test

Missing data imputation is essential task becauseremoving all records with missing values will discard useful information from other attributes. This paper estimates the performanceof prediction for autism dataset with imputed missing values. Statistical imputation methods like mean, imputation with zero or constant and machine learning imputation methods like K-nearest neighbour chained Equation methods were compared with the proposed deep learning imputation method. The predictions of patients with autistic spectrum disorder were measured using support vector machine for imputed dataset. Among the imputation methods, Deeplearningalgorithm outperformed statistical and machine learning imputation methods. The same is validated using significant difference in p values revealed using Friedman’s test

scholarly journals Comparison of Selected Multiple Imputation Methods for Continuous Variables – Preliminary Simulation Study Results

2019 ◽  
Vol 6 (339) ◽  
pp. 73-98
Author(s):  
Małgorzata Aleksandra Misztal
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Imputation Accuracy ◽  
Imputation Method ◽  
Data Sets ◽  
Continuous Variables ◽  
Imputation Methods ◽  
Study Results ◽  
Almost All

The problem of incomplete data and its implications for drawing valid conclusions from statistical analyses is not related to any particular scientific domain, it arises in economics, sociology, education, behavioural sciences or medicine. Almost all standard statistical methods presume that every object has information on every variable to be included in the analysis and the typical approach to missing data is simply to delete them. However, this leads to ineffective and biased analysis results and is not recommended in the literature. The state of the art technique for handling missing data is multiple imputation. In the paper, some selected multiple imputation methods were taken into account. Special attention was paid to using principal components analysis (PCA) as an imputation method. The goal of the study was to assess the quality of PCA‑based imputations as compared to two other multiple imputation techniques: multivariate imputation by chained equations (MICE) and missForest. The comparison was made by artificially simulating different proportions (10–50%) and mechanisms of missing data using 10 complete data sets from the UCI repository of machine learning databases. Then, missing values were imputed with the use of MICE, missForest and the PCA‑based method (MIPCA). The normalised root mean square error (NRMSE) was calculated as a measure of imputation accuracy. On the basis of the conducted analyses, missForest can be recommended as a multiple imputation method providing the lowest rates of imputation errors for all types of missingness. PCA‑based imputation does not perform well in terms of accuracy.

Reconstruction of Missing Segments in Well Data History Using Data Analytics

2021 ◽  
Author(s):  
Yuanjun Li ◽  
Roland Horne ◽  
Ahmed Al Shmakhy ◽  
Tania Felix Menchaca
Keyword(s):  
Missing Data ◽  
Data Streams ◽  
History Matching ◽  
Gas Flow ◽  
Missing Values ◽  
Imputation Accuracy ◽  
Time Span ◽  
Data Imputation ◽  
Significant Information ◽  
Missing Data Imputation

Abstract The problem of missing data is a frequent occurrence in well production history records. Due to network outage, facility maintenance or equipment failure, the time series production data measured from surface and downhole gauges can be intermittent. The fragmentary data are an obstacle for reservoir management. The incomplete dataset is commonly simplified by omitting all observations with missing values, which will lead to significant information loss. Thus, to fill the missing data gaps, in this study, we developed and tested several missing data imputation approaches using machine learning and deep learning methods. Traditional data imputation methods such as interpolation and counting most frequent values can introduce bias to the data as the correlations between features are not considered. Thus, in this study, we investigated several multivariate imputation algorithms that use the entire set of available data streams to estimate the missing values. The methods use a full suite of well measurements, including wellhead and downhole pressures, oil, water and gas flow rates, surface and downhole temperatures, choke settings, etc. Any parameter that has gaps in its recorded history can be imputed from the other available data streams. The models were tested on both synthetic and real datasets from operating Norwegian and Abu Dhabi reservoirs. Based on the characteristics of the field data, we introduced different types of continuous missing distributions, which are the combinations of single-multiple missing sections in a long-short time span, to the complete dataset. We observed that as the missing time span expands, the stability of the more successful methods can be kept to a threshold of 30% of the entire dataset. In addition, for a single missing section over a shorter period, which could represent a weather perturbation, most methods we tried were able to achieve high imputation accuracy. In the case of multiple missing sections over a longer time span, which is typical of gauge failures, other methods were better candidates to capture the overall correlation in the multivariate dataset. Most missing data problems addressed in our industry focus on single feature imputation. In this study, we developed an efficient procedure that enables fast reconstruction of the entire production dataset with multiple missing sections in different variables. Ultimately, the complete information can support the reservoir history matching process, production allocation, and develop models for reservoir performance prediction.

Data Imputation Methods for Missing Values in the Context of Clustering

2019 ◽  
pp. 240-274
Author(s):  
Mehmet S. Aktaş ◽  
Sinan Kaplan ◽  
Hasan Abacı ◽  
Oya Kalipsiz ◽  
Utku Ketenci ◽  
...  
Keyword(s):  
Missing Data ◽  
Expectation Maximization ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Real Life ◽  
Data Imputation ◽  
K Nearest Neighbor ◽  
Missing Data Imputation ◽  
Data Scarcity ◽  
Imputation Methods

Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.

scholarly journals Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

2019 ◽  
Vol 9 (1) ◽  
pp. 204 ◽  
Author(s):  
Taeyoung Kim ◽  
Woong Ko ◽  
Jinho Kim
Keyword(s):  
Power Generation ◽  
Missing Data ◽  
Missing Values ◽  
Prediction Performance ◽  
Weather Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
Pv Generation ◽  
Pv Power Generation

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.

scholarly journals Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Nurul Azifah Mohd Pauzi ◽  
Yap Bee Wah ◽  
Sayang Mohd Deni ◽  
Siti Khatijah Nor Abdul Rahim ◽  
Suhartono
Keyword(s):  
Missing Data ◽  
Simulation Study ◽  
Missing Values ◽  
Imputation Method ◽  
Quality Data ◽  
Data Imputation ◽  
Sample Sizes ◽  
Imputation Methods ◽  
Mean Imputation ◽  
Simulation Results

High quality data is essential in every field of research for valid research findings. The presence of missing data in a dataset is common and occurs for a variety of reasons such as incomplete responses, equipment malfunction and data entry error. Single and multiple data imputation methods have been developed for data imputation of missing values. This study investigated the performance of single imputation using mean and multiple imputation method using Multivariate Imputation by Chained Equations (MICE) via a simulation study. The MCAR which means missing completely at random were generated randomly for ten levels of missing rates (proportion of missing data): 5% to 50% for different sample sizes. Mean Square Error (MSE) was used to evaluate the performance of the imputation methods. Data imputation method depends on data types. Mean imputation is commonly used to impute missing values for continuous variable while MICE method can handle both continuous and categorical variables. The simulation results indicate that group mean imputation (GMI) performed better compared to overall mean imputation (OMI) and MICE with lowest value of MSE for all sample sizes and missing rates. The MSE of OMI, GMI, and MICE increases when missing rate increases. The MICE method has the lowest performance (i.e. highest MSE) when percentage of missing rates is more than 15%. Overall, GMI is more superior compared to OMI and MICE for all missing rates and sample size for MCAR mechanism. An application to a real dataset confirmed the findings of the simulation results. The findings of this study can provide knowledge to researchers and practitioners on which imputation method is more suitable when the data involves missing data.

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

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.

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