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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019230" target="_blank" >RIV/62690094:18450/22:50019230 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/9771309" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9771309</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3172319" target="_blank" >10.1109/ACCESS.2022.3172319</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

Missing values are highly undesirable in real-world datasets. The missing values should be estimated and treated during the preprocessing stage. With the expansion of nature-inspired metaheuristic techniques, interest in missing value imputation (MVI) has increased. The main goal of this literature is to identify and review the existing research on missing value imputation (MVI) in terms of nature-inspired metaheuristic approaches, dataset designs, missingness mechanisms, and missing rates, as well as the most used evaluation metrics between 2011 and 2021. This study ultimately gives insight into how the MVI plan can be incorporated into the experimental design. Using the systematic literature review (SLR) guidelines designed by Kitchenham, this study utilizes renowned scientific databases to retrieve and analyze all relevant articles during the search process. A total of 48 related articles from 2011 to 2021 were selected to assess the review questions. This review indicated that the synthetic missing dataset is the most popular baseline test dataset to evaluate the effectiveness of the imputation strategy. The study revealed that missing at random (MAR) is the most common proposed missing mechanism in the datasets. This review also indicated that the hybridizations of metaheuristics with clustering or neural networks are popular among researchers. The superior performance of the hybrid approaches is significantly attributed to the power of optimized learning in MVI models. In addition, perspectives, challenges, and opportunities in MVI are also addressed in this literature. The outcome of this review serves as a toolkit for the researchers to develop effective MVI models.

Název v anglickém jazyce

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

Popis výsledku anglicky

Missing values are highly undesirable in real-world datasets. The missing values should be estimated and treated during the preprocessing stage. With the expansion of nature-inspired metaheuristic techniques, interest in missing value imputation (MVI) has increased. The main goal of this literature is to identify and review the existing research on missing value imputation (MVI) in terms of nature-inspired metaheuristic approaches, dataset designs, missingness mechanisms, and missing rates, as well as the most used evaluation metrics between 2011 and 2021. This study ultimately gives insight into how the MVI plan can be incorporated into the experimental design. Using the systematic literature review (SLR) guidelines designed by Kitchenham, this study utilizes renowned scientific databases to retrieve and analyze all relevant articles during the search process. A total of 48 related articles from 2011 to 2021 were selected to assess the review questions. This review indicated that the synthetic missing dataset is the most popular baseline test dataset to evaluate the effectiveness of the imputation strategy. The study revealed that missing at random (MAR) is the most common proposed missing mechanism in the datasets. This review also indicated that the hybridizations of metaheuristics with clustering or neural networks are popular among researchers. The superior performance of the hybrid approaches is significantly attributed to the power of optimized learning in MVI models. In addition, perspectives, challenges, and opportunities in MVI are also addressed in this literature. The outcome of this review serves as a toolkit for the researchers to develop effective MVI models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

10

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

61544-61566

Kód UT WoS článku

000811547200001

EID výsledku v databázi Scopus

2-s2.0-85132530313