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Missing Value Imputation for Diabetes Prediction
2022 International Joint Conference on Neural Networks (IJCNN), 2022This research is supported, in part, by the National Research Foundation (NRF), Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003). H. Qian thanks the support from the Wallenberg-NTU Presidential Postdoctoral Fellowship.
Fei Luo +8 more
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On-line imputation for missing values
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017Missing values are widespread in many real world applications. It is often preferred to receive in real-time the high quality complete tuples, rather than an incomplete one containing null attribute values. The requirements for high quality and real-time response make the task of missing value imputation much challenging.
Fengfeng Fan +2 more
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Missing values prediction with K2
Intelligent Data Analysis, 2002Dealing with missing values is one important task in data mining. There are many ways to work with this kind of data, but the literature doesn't determine the best one to all kinds of data set. The aim of this work is to show the application of a bayesian algorithm (K2) in data mining problems as a data preparation and classification tool.
Estevam R. Hruschka Jr. +1 more
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Incomplete Databases: Missing Records and Missing Values
2012Data completeness is an essential aspect of data quality as in many scenarios it is crucial to guarantee the completeness of query answers. Data might be incomplete in two ways: records may be missing as a whole, or attribute values of a record may be absent, indicated by a null.
Werner Nutt +2 more
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Recommending missing sensor values
2015 IEEE International Conference on Big Data (Big Data), 2015Datasets gathered from sensor networks often suffer from a significant fraction of missing data, due to issues such as communication and sensor interference, power depletion, and hardware failure. Many standard data analysis tools such as classification engines, time-sequence pattern analysis modules, and statistical tools are ill-equipped to deal with
Chung-Yi Li +6 more
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Sequential imputation for missing values
Computational Biology and Chemistry, 2007As missing values are often encountered in gene expression data, many imputation methods have been developed to substitute these unknown values with estimated values. Despite the presence of many imputation methods, these available techniques have some disadvantages. Some imputation techniques constrain the imputation of missing values to a limited set
Sabine Verboven +2 more
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Ordered Estimation of Missing Values
1999When attempting to discover by learning concepts embedded in data, it is not uncommon to find that information is missing from the data. Such missing information can diminish the confidence on the concepts learned from the data. This paper describes a new approach to fill missing values in examples provided to a learning algorithm.
Oscar Ortega Lobo, Masayuki Numao
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Outfluence – The impact of missing values
Model Assisted Statistics and Applications, 2008There are numerous measures that assess the effect of an observation, group of observations, a variable, or variables and observations on the regression estimation. Incomplete data is a common difficulty in data analysis. We introduce a new measure which assesses the effect of a missing observation, a group of missing observations, an incomplete ...
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A review on missing values for main challenges and methods
Information Systems, 2023Ren Lijuan
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Missing data in surveys: Key concepts, approaches, and applications
Research in Social and Administrative Pharmacy, 2022Ardalan Mirzaei +2 more
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