Approaches to Dealing With Missing Data in Railway Asset Management

McMahon, Paul; Zhang, Tieling; Dwight, Richard

doi:10.1109/access.2020.2978902

Cited by 13 publications

(4 citation statements)

References 79 publications

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“…The data that are missing completely at random have the statistical benefit of the analysis remaining fair. [24] exemplifies techniques to deal with MCAR imputation K-nearest neighbors, statistical analysis of data set, and the identification of similar assets from a data set population. 2.…”

Section: Types Of Missing Datamentioning

confidence: 99%

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Rahmany¹,

Mahfoudhi²,

Freihat³

et al. 2022

AAIML

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Diabetes mellitus is a set of metabolic illnesses characterized by abnormally high blood sugar levels. In 2017, 8.8% of the world’s population had diabetes. By 2045, it is expected that this percentage will have risen to approximately 10%. Missing data, a prevalent problem even in a well-designed and controlled study, can have a major impact on the conclusions that can be derived from the available data. Missing data may decrease a study’s statistical validity and lead to erroneous results due to distorted estimations. In this study, we hypothesize that (a) replacing missing values using machine learning techniques rather than the mean value and group mean value and (b) using SVM kernel RBF classifier will result in the highest level of accuracy in comparison to traditional techniques such as DT, RF, NB, SVM, AdaBoost, and ANN. The classification results improved significantly when using regression to replace the missing values over the group median or the mean. This is a 10% improvement over previously developed strategies that have been reported in the literature.

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Section: Types Of Missing Datamentioning

confidence: 99%

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Rahmany¹,

Mahfoudhi²,

Freihat³

et al. 2022

AAIML

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“…LIN [4] comments that if the MVs rate is less than 10% or 15%, they can be removed without causing any significant loss to the mining process. However, this does not mean that the datasets in any problem domain must follow this rule; in other words, small amounts of missing data may contain essential information that must be managed [9]. In addressing this issue, the literature suggests using missing data imputation methods, which involve replacing missing data with actual (plausible) values.…”

Section: Introductionmentioning

confidence: 99%

EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm

Jacob Junior,

do Carmo,

de Santana

et al. 2024

PLoS ONE

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Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be “drama” and “bibliography” simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.

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“…It was found that the proposed model can accurately impute missing data. McMahon et al ( 13 ) identified the types of missing data in railway asset management and developed a long short-term memory (LSTM) model ( 14 ) for imputation of missing data. It was found that the LSTM model is suitable for scenarios where missing data is not completely random and has a strong time-series dependency.…”

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confidence: 99%

Missing Pavement Performance Data Imputation Using Graph Neural Networks

Gao

2022

Transportation Research Record: Journal of the Transportation R

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Pavement condition data is important for providing information on the current state of the network and determining the needs of preventive maintenance or rehabilitation treatments. However, the condition data set is often incomplete for various reasons such as measurement errors and non-periodic inspection intervals. Missing data, especially when missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing practices in pavement management systems (PMS) usually discard entire cases with missing data or impute it through data correlation. This paper proposes a graph-based deep learning framework, convolutional graph neural networks, to tackle the missing data problem in PMS. Unlike other variants of neural networks, the proposed approach is able to capture the spatio-temporal relationship in data and to learn and reconstruct the missing data by combining information among neighboring sections. In the case study, pavement condition data from 4,446 sections managed by Texas Department of Transportation were used. Experiments show that the proposed model was able to outperform standard machine learning models when imputing the missing data.

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Approaches to Dealing With Missing Data in Railway Asset Management

Cited by 13 publications

References 79 publications

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Missing Data Recovery in the E-health Context Based on Machine Learning Models

EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm

Missing Pavement Performance Data Imputation Using Graph Neural Networks

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