A survey on missing data in machine learning
Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …
from data and often a problem of missing values is encountered. Missing values occur …
Missing value imputation: a review and analysis of the literature (2006–2017)
WC Lin, CF Tsai - Artificial Intelligence Review, 2020 - Springer
Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …
solution method for incomplete dataset problems, specifically those where some data …
Learning k for kNN Classification
The K Nearest Neighbor (kNN) method has widely been used in the applications of data
mining and machine learning due to its simple implementation and distinguished …
mining and machine learning due to its simple implementation and distinguished …
Missing data imputation using statistical and machine learning methods in a real breast cancer problem
JM Jerez, I Molina, PJ García-Laencina, E Alba… - Artificial intelligence in …, 2010 - Elsevier
OBJECTIVES: Missing data imputation is an important task in cases where it is crucial to use
all available data and not discard records with missing values. This work evaluates the …
all available data and not discard records with missing values. This work evaluates the …
[HTML][HTML] A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients
Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular
Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess …
Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess …
A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting
Accurate and timely traffic flow prediction is crucial to proactive traffic management and
control in data-driven intelligent transportation systems (D 2 ITS), which has attracted great …
control in data-driven intelligent transportation systems (D 2 ITS), which has attracted great …
Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values
Breast cancer is the most frequently diagnosed cancer in women. Using historical patient
information stored in clinical datasets, data mining and machine learning approaches can …
information stored in clinical datasets, data mining and machine learning approaches can …
Missing data imputation by K nearest neighbours based on grey relational structure and mutual information
R Pan, T Yang, J Cao, K Lu, Z Zhang - Applied Intelligence, 2015 - Springer
Abstract Treatment of missing data has become increasingly significant in scientific research
and engineering applications. The classic imputation strategy based on the K nearest …
and engineering applications. The classic imputation strategy based on the K nearest …
Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model
AM Sefidian, N Daneshpour - Expert Systems with Applications, 2019 - Elsevier
The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …
an inevitable one. Therefore, missing values should be handled carefully before the mining …
Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening
Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High
costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity …
costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity …