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Learning with Missing Data [PDF]

2020 IEEE International Conference on Big Data (Big Data), 2020
Many real-world data sets contain missing values, therefore, learning with incomplete data sets is a common challenge faced by data scientists. Handling them in an intelligent way is important to develop robust data models, since there is no perfect approach to compensate for the missing values.
Carlos A. Escobar, Jorge Arinez 0001, Daniela Macias, Rubén Morales-Menéndez   +3 more
openaire   +1 more source

Block-Conditional Missing at Random Models for Missing Data [PDF]

, 2010
Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the
John D. Kalbfleisch, Roderick J. A. Little, Roderick J. A. Little Is, Yan Zhou   +3 more
core   +1 more source

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 Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

Mathematics, 2022
A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed.
Tuo Sun, Shihao Zhu, Ruochen Hao, Bo Sun, Jiemin Xie   +4 more
doaj   +1 more source

Missing Data or Not?

Diseases of the Colon & Rectum, 2015
restriction
Jeonghyun, Kang, Kang Young, Lee
openaire   +3 more sources

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

Using paradata for imputation of missing values in sociological survey data: results of statistical modeling (case of Croatia and Slovakia) [PDF]

Соціологія
Missing values are a common issue in quantitative social researches. One of the ways to handle missing data is by data imputation. This article outlines the challenges of traditional data imputation methods, which often introduce biases, and presents an ...
Andrii Gorbachyk, Yaroslav Kostenko
doaj   +1 more source