Results 41 to 50 of about 16,362,739 (323)
Advanced methods for missing values imputation based on similarity learning
PeerJ Computer Science, 2021 The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values.
K. Fouad +3 more
semanticscholar +1 more source
Missing-Values Adjustment for Mixed-Type Data
Journal of Probability and Statistics, 2011 We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record.
Agostino Tarsitano, Marianna Falcone
doaj +1 more source
Environmental Health Engineering and Management, 2021 Background: PIn air quality studies, it is very often to have missing data due to reasons such as machine failure or human error. The approach used in dealing with such missing data can affect the results of the analysis.
Parisa Saeipourdizaj +2 more
semanticscholar +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 +60 more
core +1 more source
Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
doaj +1 more source
Lookahead selective sampling for incomplete data
International Journal of Applied Mathematics and Computer Science, 2016 Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values.
Abdallah Loai, Shimshoni Ilan
doaj +1 more source
Preserving Logical Relations while Estimating Missing Values [PDF]
Revista Română de Statistică, 2017 Item-nonresponse is often treated by means of an imputation technique. In some cases, the data have to satisfy certain constraints, which are frequently referred to as edits.
Ton de Waal, Wieger Coutinho
doaj
A Self-Attention-Based Imputation Technique for Enhancing Tabular Data Quality
Data, 2023 Recently, data-driven decision-making has attracted great interest; this requires high-quality datasets. However, real-world datasets often feature missing values for unknown or intentional reasons, rendering data-driven decision-making inaccurate.
Do-Hoon Lee, Han-joon Kim
doaj +1 more source
Approximating Clustering of Fingerprint Vectors with Missing Values
, 2005 The problem of clustering fingerprint vectors is an interesting problem in Computational Biology that has been proposed in (Figureroa et al. 2004). In this paper we show some improvements in closing the gaps between the known lower bounds and upper ...
A. Figueroa +13 more
core +1 more source
A Note on likelihood estimation of missing values in time series [PDF]
, 1991 Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood, or as random variables and predicted by the expectation of the unknown values given the data.
Peña, Daniel, Tiao, George C.
core +1 more source