Results 11 to 20 of about 4,202,897 (320)

Improving Missing Data Imputation with Deep Generative Models [PDF]

, 2019
Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the problem of imputing missing values based on deep generative models. Previous
Ramiro Daniel Camino, Christian Hammerschmidt, Radu State   +2 more
openalex   +3 more sources

Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques [PDF]

, 2019
Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing.
Chen, Po-Yi, Garnier-Villarreal, Mauricio, Jia, Fan, Kite, Benjamin Arthur, Wu, Wei   +4 more
core   +2 more sources

Improving accuracy of missing data imputation in data mining

Kurdistan Journal of Applied Research, 2017
In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always
Nzar A. Ali, Zhyan M. Omer
doaj   +1 more source

Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation [PDF]

, 2008
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world
Albrecht, Austin, Baird, Batista, Boehm, Boehm, Breiman, Briand, Briand, Briand, Brockmeier, Cartwright, Cheung, Clark, Feelders, Finnie, Gama, Gray, Holte, Jain, Jeffery, Jun Liu, Jönsson, Kemerer, Khotanzad, Kibler, Kim, Kitchenham, Kohavi, Little, Little, Little, Little, Little, Martin Shepperd, Miranda, Myrtveit, Pickard, Putnam, Qinbao Song, Quinlan, Robins, Rubin, Rubin, Rubin, Rubin, Samson, Selby, Shao, Shepperd, Shepperd, Siedelecki, Song, Song, Srinivasan, Strike, Tabachnick, Tay, Walkerden, Walston, Xiangru Chen   +60 more
core   +1 more source

Providing an imputation algorithm for missing values of longitudinal data using Cuckoo search algorithm: A case study on cervical dystonia

Electronic Physician, 2017
Background: Missing values in data are found in a large number of studies in the field of medical sciences, especially longitudinal ones, in which repeated measurements are taken from each person during the study.
Amin Golabpour, Kobra Etminani, Hassan Doosti, Hamid Heidarian Miri, Reza Ghanbari   +4 more
doaj   +1 more source

Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

IEEE Access, 2020
Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data ...
Qin Li, Huachun Tan, Yuankai Wu, Linhui Ye, Fan Ding   +4 more
doaj   +1 more source

Joint Models for Incomplete Longitudinal Data and Time-to-Event Data

Mathematics, 2022
Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data.
Yuriko Takeda, Toshihiro Misumi, Kouji Yamamoto   +2 more
doaj   +1 more source

Random Forest variable importance with missing data [PDF]

, 2012
Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander, Hothorn, Torsten, Ulm, Kurt   +2 more
core   +1 more source

Missing data imputation using classification and regression trees [PDF]

PeerJ Computer Science
Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
doaj   +2 more sources

Inference and Missing Data [PDF]

Biometrika, 1975
ABSTRACTTwo results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then ...
openaire   +2 more sources