Missing Value Imputation Designs and Methods of Nature-Inspired Metaheuristic Techniques: A Systematic Review

Chiu, Po Chan; Selamat, Ali; Krejcar, Ondřej; Kuok, King Kuok; Bujang, Siti Dianah Abdul; Fujita, Hamido

doi:10.1109/access.2022.3172319

Cited by 8 publications

(12 citation statements)

References 77 publications

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“…All these challenges have amplified the complexity associated with handling MVs. Nevertheless, finding studies that relate MLC and MV is not straightforward, as demonstrated in [4,8,17].…”

Section: Plos Onementioning

confidence: 99%

“…These steps are repeated until the population is complete. Afterward, the mutation follows the established rate (lines [15][16][17][18][19][20]. The new population is arranged, and the iterative process continues until the stopping criterion is reached.…”

Section: The Evoimp Algorithmmentioning

confidence: 99%

“…To reduce the missing values prediction error, using metaheuristics could optimize the value that would be imputed [15]. Notably, bioinspired strategies such as Genetic Algorithms (GAs) are prominent in optimizing solutions [17].…”

Section: Introductionmentioning

confidence: 99%

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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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“…All these challenges have amplified the complexity associated with handling MVs. Nevertheless, finding studies that relate MLC and MV is not straightforward, as demonstrated in [4,8,17].…”

Section: Plos Onementioning

confidence: 99%

Section: The Evoimp Algorithmmentioning

confidence: 99%

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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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“…Prior to imputation, it is necessary to ensure that there is a sufficient amount of data for imputation. If a column has more than 50% of data missing, then it is dropped, and the attempt for imputation is dismissed. , Missingno and Bilogur’s method was used to illustrate the missing values. Missing values from the database are shown in Figure .…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models

et al. 2022

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Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data pre-processing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C 5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.

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“…However, this method has risks of losing important information in datasets, and it can significantly impact classification accuracy. Many other methods have been used and proposed in the literature to impute missing data for increasing classification accuracy [8], [9]. These methods can be classified into two principal categories; statistical-based methods, such as mean/mod and least squares (LS), and machinelearning-based methods like k-nearest neighbors (KNN), neural networks (NN), and decision tree (DT) [10].…”

Section: Introductionmentioning

confidence: 99%

A method for missing values imputation of machine learning datasets

Hanyf,

Silkan

2024

IJ-AI

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<p>In machine learning applications, handling missing data is often required in the pre-processing phase of datasets to train and test models. The class center missing value imputation (CCMVI) is among the best imputation literature methods in terms of prediction accuracy and computing cost. The main drawback of this method is that it is inadequate for test datasets as long as it uses class centers to impute incomplete instances because their classes should be assumed as unknown in real-world classification situations. This work aims to extend the CCMVI method to handle missing values of test datasets. To this end, we propose three techniques: the first technique combines the CCMVI with other literature methods, the second technique imputes incomplete test instances based on their nearest class center, and the third technique uses the mean of centers of classes. The comparison of classification accuracies shows that the second and third proposed techniques ensure accuracy close to that of the combination of CCMVI with literature imputation methods, namely k-nearest neighbors (KNN) and mean methods. Moreover, they significantly decrease the time and memory space required for imputing test datasets.</p>

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Missing Value Imputation Designs and Methods of Nature-Inspired Metaheuristic Techniques: A Systematic Review

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 8 publications

References 77 publications

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

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

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models

A method for missing values imputation of machine learning datasets

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