Results 11 to 20 of about 131,123 (260)
مدیریت اطلاعات سلامت, 2021 Introduction: Imputation of missing values in a medical data set is one of the important challenges in data mining. Therefore, this study was performed with the aim of imputation the missing values of some features of the diabetes and breast cancer ...
Elham Pourjani +2 more
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Missing Value Imputation with Unsupervised Backpropagation [PDF]
Computational Intelligence, 2014 AbstractMany data mining and data analysis techniques operate on dense matrices or complete tables of data. Real‐world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy.
Michael S. Gashler +3 more
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Algorithms, 2023 Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life.
Cong Li, Xupeng Ren, Guohui Zhao
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Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent
Sensors, 2022 Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce
Hu Pan +7 more
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Methylation data imputation performances under different representations and missingness patterns
BMC Bioinformatics, 2020 Background High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data.
Pietro Di Lena +3 more
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Entropy, 2023 Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable.
Philip Buczak +2 more
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Kernel weighted least square approach for imputing missing values of metabolomics data
Scientific Reports, 2021 Mass 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 ...
Nishith Kumar +2 more
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MIAEC: Missing Data Imputation Based on the Evidence Chain
IEEE Access, 2018 Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu +4 more
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IJAIN (International Journal of Advances in Intelligent Informatics), 2021 K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly.
Nadzurah Zainal Abidin +1 more
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Adaptive multiple imputations of missing values using the class center
Journal of Big Data, 2022 Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values.
Kritbodin Phiwhorm +4 more
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