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 …
[HTML][HTML] Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)
Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …
intending the primary solution scheme for the datasets containing one or more missing …
[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 …
Predicting breast cancer recurrence using machine learning techniques: a systematic review
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically
related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast …
related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast …
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 …
[HTML][HTML] A joint learning Im-BiLSTM model for incomplete time-series Sentinel-2A data imputation and crop classification
B Chen, H Zheng, L Wang, O Hellwich, C Chen… - International Journal of …, 2022 - Elsevier
Multi-temporal deep learning approaches can make full use of crop growth patterns and
phenological characteristics, resulting in excellent crop classification performance in large …
phenological characteristics, resulting in excellent crop classification performance in large …
Generating synthetic missing data: A review by missing mechanism
MS Santos, RC Pereira, AF Costa, JP Soares… - IEEE …, 2019 - ieeexplore.ieee.org
The performance evaluation of imputation algorithms often involves the generation of
missing values. Missing values can be inserted in only one feature (univariate configuration) …
missing values. Missing values can be inserted in only one feature (univariate configuration) …
Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns
EL Silva-Ramírez, R Pino-Mejías… - Applied Soft Computing, 2015 - Elsevier
The knowledge discovery process is supported by data files information gathered from
collected data sets, which often contain errors in the form of missing values. Data imputation …
collected data sets, which often contain errors in the form of missing values. Data imputation …
Imputations of missing values using a tracking-removed autoencoder trained with incomplete data
X Lai, X Wu, L Zhang, W Lu, C Zhong - Neurocomputing, 2019 - Elsevier
The presence of missing values in incomplete datasets increases the difficulty of data
mining. In this paper, we use the autoencoder (AE) to model the incomplete data for …
mining. In this paper, we use the autoencoder (AE) to model the incomplete data for …
[PDF][PDF] Predicting cervical cancer using machine learning methods
R Alsmariy, G Healy… - International Journal of …, 2020 - pdfs.semanticscholar.org
In almost all countries, precautionary measures are less expensive than medical treatment.
The early detection of any disease gives a patient better chances of successful treatment …
The early detection of any disease gives a patient better chances of successful treatment …