4 results for “Imputation Process”

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Research ArticleOpen accessIn the keywordsLow-Sampling Trajectory Reconstruction using Criteria-Based Routing over a GraphEdison Ospina, Francisco Moreno, Jaime GuzmánLocation-based services mainly provide geo-location data. However, a moving object’s detailed trajectory route is lost when there is low-sampling of these location data. Previous works have been developed in order to find the possible trajectories by using the location history logged by users. These methods can be considered as reconstruction or imputation processes. In this study, we reconstruct trajectories using … …American Journal of Applied Sciences, Vol. 13, Issue 2 · 3 Feb 2016 · 7,455 accessesView PDF Cite

Research ArticleOpen accessIn the abstractMissing Values Treatment and Feature Reduction Analysis to Enhance ClassificationD. Muralidharan, K. Renuka, Mulagala Jaswant et al.Datasets may have large number of features which makes it hard and time consuming to classify. Additionally, they may have irrelevant and noise features too with missing values. The missing values should be treated in a proper way so that the classifier accuracy can be improved. There is also a need to reduce features and select only the features necessary … …Journal of Computer Science, Vol. 16, Issue 2 · 20 Feb 2020 · 8,114 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA MIXTURE DROPOUT MECHANISM IN A LONGITUDINAL STUDY WITH TWO TIME POINTS: A METHADONE STUDYZohreh Toghrayee, Parvin Jalili, Heng Chin Low, Ardavan TaghvaOne of the most important issues that confront statisticians in longitudinal studies is dropouts. A variety of reasons may lead to withdrawal from a study and produce two different missingness mechanisms, namely, missing at random and non-ignorable dropouts. Nevertheless, none of these mechanisms is tenable in most studies. In addition, it may be that not all of dropouts are nonignorable. … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 26 Apr 2014 · 6,724 accessesView PDF Cite

Research ArticleOpen accessIn the abstractRadial Basis Function Network Dependent Exclusive Mutual Interpolation for Missing Value ImputationR. S. Somasundaram, R. NedunchezhianThe success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a … …Journal of Computer Science, Vol. 9, Issue 3 · 24 Apr 2013 · 7,002 accessesView PDF Cite