Results 51 to 60 of about 24,713,244 (334)

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

MissForest - nonparametric missing value imputation for mixed-type data

, 2011
Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. J. Stekhoven, Harley, Kurgan, Latal, LITTLE, Oba, P. Buhlmann, Smit, Troyanskaya, van Buuren, Wille, Wu   +11 more
core   +1 more source

Reuse of imputed data in microarray analysis increases imputation efficiency [PDF]

BMC Bioinformatics, 2004
Abstract Background The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set.
Ki-Yeol Kim, Byoung-Jin Kim, Gwan-Su Yi
openaire   +4 more sources

Evaluating the state of the art in missing data imputation for clinical data

Briefings Bioinform., 2021
Clinical data are increasingly being mined to derive new medical knowledge with a goal of enabling greater diagnostic precision, better-personalized therapeutic regimens, improved clinical outcomes and more efficient utilization of health-care resources.
Yuan Luo
semanticscholar   +1 more source

Context-Aware Data Imputation: Application of Domain-Agnostic Deep Imputation Network

IEEE Access
Data imputation (DI) is a crucial task to manage missing data across different domains, such as healthcare and finance. Traditional imputation methods often fail to account for contextual nuances within specific domains due to the heterogeneity of data ...
Mohammed Gh. Al Zamil, Omar Khalid Alomeir   +1 more
doaj   +1 more source

Fast and accurate imputation of summary statistics enhances evidence of functional enrichment

, 2013
Imputation using external reference panels is a widely used approach for increasing power in GWAS and meta-analysis. Existing HMM-based imputation approaches require individual-level genotypes.
Bhatia, Gaurav, Gusev, Alexander, Hirschhorn, Joel, Pasaniuc, Bogdan, Patterson, Nick, Pickrell, Joseph, Price, Alkes L., Shi, Huwenbo, Strachan, David P, Zaitlen, Noah   +9 more
core   +2 more sources

Nearest neighbours in least-squares data imputation algorithms with different missing patterns [PDF]

, 2006
Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experimentally compared.
Atkeson, Berry, Boris Mirkin, Davies, Dempster, Everrit, Gabriel, Golub, Grung, Heiser, Holter, Holzinger, Hunt, Ito Wasito, Jollife, Kamakashi, Kenney, Kiers, Krzanowski, Laaksonen, Little, Mirkin, Myrtveit, Roweis, Rubin, Rubin, Schafer, Shum, Tipping, Troyanskaya, Wold   +30 more
core   +1 more source

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

PLoS ONE, 2020
BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll, J Turicchi, C Duarte, J Michalowska, S C Larsen, A L Palmeira, B L Heitmann, G W Horgan, R J Stubbs   +8 more
doaj   +1 more source

Integration of survey data and big observational data for finite population inference using mass imputation [PDF]

, 2018
Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining a probability sample with big observational
Kim, Jae Kwang, Kim, Jae Kwang, Yang, Shu   +2 more
core   +3 more sources

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

Molecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns, Jaclyn S. Long, Sergio Lilla, Kelly Hodge, Marcus J. G. W. Ladds, Colin Nixon, Paula Fernández‐Palanca, Sara Zanivan, Pilar Acedo, Stephen P. Pereira, Kevin M. Ryan   +10 more
wiley   +1 more source