Unsupervised data imputation with multiple importance sampling variational autoencoders by Shenfen Kuang, Yewen Huang, Jie Song

Subjects: “…Missing data…”
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Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence by Daniel Wrench, Tulasi N. Parashar, Ritesh K. Singh, Marcus Frean, Ramesh Rayudu

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Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks by Aboulfazl Ebrahimi, Mahboubeh Shamsi, Morteza Mohajjel

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Big Data Cleaning Based on Improved CLOF and Random Forest for Distribution Networks by Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng

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Processing Method of Missing Monitoring Data of Concrete Dam Deformation Based on GRU by ZHAI Junjie, SHI Zhongwen, LI Jun, PAN Wenming, JIA Hailei, YE Sheng

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How much missing data is too much to impute for longitudinal health indicators?... by K. P. Junaid, Tanvi Kiran, Madhu Gupta, Kamal Kishore, Sujata Siwatch

“…Abstract Background The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. …”
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

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Spatiotemporal variation in size-dependent growth rates in small isolated populations of Arctic charr (Salvelinus alpinus) by Elizabeth A. Mittell, Camille A. Leblanc, Bjarni K. Kristjánsson, Moira M. Ferguson, Katja Räsänen, Michael B. Morrissey

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Sparse Aperture Inverse Synthetic Aperture Radar Imaging for Maneuvering Targets With Migration Through Resolution Cells Correction by Jiaxing Yang, Yong Wang

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EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems by Kevin Z. Bai, John M. Fossaceca

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Modified Weights-of-Evidence Modeling with Example of Missing Geochemical Data by Daojun Zhang, Frits Agterberg

“…WofE allows construction of input layers that have missing data as a separate category in addition to known presence-absence type input, while logistic regression as such is not capable of handling missing data. …”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…Five scenarios of missing data (10%, 20%, 30%, 40%, 50%) were simulated, in which datasets were randomly withdrawn from the ETo base. …”
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Scattered Data Processing Approach Based on Optical Facial Motion Capture by Qiang Zhang, Xiaoying Liang, Xiaopeng Wei

“…Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. …”
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Fuzzy evaluation for response effectiveness in cases of incomplete information by Fenghua LI, Yongjun LI, Zhengkun YANG, Han ZHANG, Lingcui ZHANG

“…,missing elements of judgment matrix and missing data of indicators) and the response effectiveness was evaluate.Firstly,a hierarchical indicator tree was design to characterize the effectiveness from the perspectives of both attack and defense.Then,the fuzzy analytic hierarchy process (FAHP) was used to calculate the comprehensive weight of each indicator.Finally,the response effectiveness was calculated using fuzzy comprehensive evaluation.In particular,to deal with the problem of incompleteness of fuzzy judgment matrix in the process of FAHP,the missing elements were completed based on the transitivity of elements.And to deal with the problem of loss data in the comprehensive evaluation,the missing data was completed based on matrix completion.The experimental results show that the proposed scheme can accurately recover the missing data and can effectively evaluate the effectiveness of response.…”
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A Chain Ratio Exponential-Type Compromised Imputation for Mean Estimation: Case Study on Ozone Pollution in Saraburi, Thailand by Kanisa Chodjuntug, Nuanpan Lawson

“…We need to deal with missing data in a proper way before analysis using standard statistical techniques. …”
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A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data by Yuxiang ZHAO, Guangyue LU, Hanglong WANG, Siwei LI

“…Aiming at prediction of telecom customer churn,a novel method was proposed to increase the prediction accuracy with the missing data based on the Bayesian network.This method used k-nearest neighbor algorithm to fill the missing data and adds two types of monotonic influence constraints into the process of learning Bayesian network parameter.Simulations and actual data analysis demonstrate that the proposed algorithm obtains higher prediction accuracy of churn customers with the loss of less cost prediction accuracy of loyal customers,outperforms the classic expectation maximization algorithm.…”
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Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable by Sara Javadi, Abbas Bahrampour, Mohammad Mehdi Saber, Behshid Garrusi, Mohammad Reza Baneshi

“…Multiple imputation by chained equations (MICE) is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. …”
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Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease by Dennis Shen, Anish Agarwal, Vishal Misra, Bjoern Schelter, Devavrat Shah, Helen Shiells, Claude Wischik

“…At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. …”
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K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data by Ayesha Sania, Ayesha Sania, Nicolò Pini, Nicolò Pini, Morgan E. Nelson, Michael M. Myers, Michael M. Myers, Lauren C. Shuffrey, Maristella Lucchini, Maristella Lucchini, Amy J. Elliott, Amy J. Elliott, Hein J. Odendaal, William P. Fifer, William P. Fifer

“…Since participants with no missing days were not comparable to those with missing data, segments of non-missing data from all participants were included as a reference. …”
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TEC Map Completion Using DCGAN and Poisson Blending by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…The results with random masks (15–40% missing data) show that DCGAN‐PB can achieve better TEC map completion than DCGAN alone, and more training data can significantly improve its generalization. …”
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