Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho, Heru; Utama, Nugraha Priya; Surendro, Kridanto

doi:10.21203/rs.3.rs-107394/v1

Cited by 3 publications

(11 citation statements)

References 30 publications

(55 reference statements)

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“…The value of RMSE and r was better, compared to the previous study [42]. Based on the simulation results, the Pearson correlation coefficient (r) value was close to 1, while root mean squared error (RMSE) was close to 0.…”

Section: Outlier Removal's + Normalization + Imputationmentioning

confidence: 60%

“…This study proposed combination of normalization and outlier removals and normalization on class center-based Firefly Algorithm. In addition, it was based on preliminary studies conducted by the author [42]. The fireflies' pattern with a lower light intensity was used, while the group of fireflies with a lower intensity were approximated when the missing data was entered.…”

Section: Normalization and Outlier Removal In Class Center-based Firefly Algorithm For Missing Value Imputationmentioning

confidence: 99%

“…The pseudocode below summarized the Firefly Algorithm's main steps for handling missing values based on class center [42].…”

Section: Normalization and Outlier Removal In Class Center-based Firefly Algorithm For Missing Value Imputationmentioning

confidence: 99%

“…Previous studies have produced class center-cased firefly algorithm for handling missing values [42], as the development of methods by considering correlation [43]. However, they have failed to consider data normalization and outlier detection before performing the imputation process.…”

Section: Introductionmentioning

confidence: 99%

“…This study proposed a combination of normalization and outlier removals as well as normalization on class center-based Firefly Algorithm. It was also developed from preliminary studies conducted by the author [42]. This contributed an evaluation of the normalization and outlier detection combination's impact on several missing data imputation methods, mean imputation (Mean), random value imputation (Rand), linear regression imputation (Reg), multiple imputation (MI), KNN imputation, decision tree (DT) imputation.…”

Section: Introductionmentioning

confidence: 99%

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Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

2021

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A missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study’s statistical power or result in inaccurate estimations and conclusions. Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features. In cases where the observed data contained outliers, the missing value estimated results are sometimes unreliable or even differ greatly from the true values. Therefore, this study aims to propose the combination of normalization and outlier removals before imputing missing values on the class center-based firefly algorithm method (ON + C3FA). Moreover, some standard imputation techniques like mean, a random value, regression, as well as multiple imputation, KNN imputation, and decision tree (DT)-based missing value imputation were utilized as a comparison of the proposed method. Experimental results on the sonar dataset showed normalization and outlier removals effect in the methods. According to the proposed method (ON + C3FA), AUC, accuracy, F1-Score, Precision, Recall, and AUC-PR had 0.972, 0.906, 0.906, 0.908, 0.906, 0.61 respectively. The result showed combining normalization and outlier removals in C3-FA (ON + C3FA) was an efficient technique for obtaining actual data in handling missing values, and it also outperformed the previous studies methods with r and RMSE values of 0.935 and 0.02. Meanwhile, the Dks value obtained from this technique was 0.04, which indicated that it could maintain the values or distribution accuracy.

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Section: Outlier Removal's + Normalization + Imputationmentioning

confidence: 60%

Section: Normalization and Outlier Removal In Class Center-based Firefly Algorithm For Missing Value Imputationmentioning

confidence: 99%

“…The pseudocode below summarized the Firefly Algorithm's main steps for handling missing values based on class center [42].…”

Section: Normalization and Outlier Removal In Class Center-based Firefly Algorithm For Missing Value Imputationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

2021

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Adaptive multiple imputations of missing values using the class center

et al. 2022

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Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values. Missing value imputation is the primary method for resolving problems involving incomplete datasets. Missing attribute values are replaced with values from a selected set of observed data using statistical or machine learning methods. Although machine learning techniques can generate reasonably accurate imputation results, they typically require longer imputation durations than statistical techniques. This study proposes the adaptive multiple imputations of missing values using the class center (AMICC) approach to produce effective imputation results efficiently. AMICC is based on the class center and defines a threshold from the weighted distances between the center and other observed data for the imputation step. Additionally, the distance can be an adaptive nearest neighborhood or the center to estimate the missing values. The experimental results are based on numerical, categorical, and mixed datasets from the University of California Irvine (UCI) Machine Learning Repository with introduced missing values rate from 10 to 50% in 27 datasets. The proposed AMICC approach outperforms the other missing value imputation methods with higher average accuracy at 81.48% which is higher than those of other methods about 9 – 14%. Furthermore, execution time is different from the Mean/Mode method, about seven seconds; moreover, it requires significantly less time for imputation than some machine learning approaches about 10 – 14 s.

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Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable

2023

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One of the most common causes of incompleteness is missing data, which occurs when no data value for the variables in observation is stored. An adaptive approach model outperforming other numerical methods in the classification problem was developed using the class center-based Firefly algorithm by incorporating attribute correlations into the imputation process (C3FA). However, this model has not been tested on categorical data, which is essential in the preprocessing stage. Encoding is used to convert text or Boolean values in categorical data into numeric parameters, and the target encoding method is often utilized. This method uses target variable information to encode categorical data and it carries the risk of overfitting and inaccuracy within the infrequent categories. This study aims to use the smoothing target encoding (STE) method to perform the imputation process by combining C3FA and standard deviation (STD) and compare by several imputation methods. The results on the tic tac toe dataset showed that the proposed method (C3FA-STD) produced AUC, CA, F1-Score, precision, and recall values of 0.939, 0.882, 0.881, 0.881, and 0.882, respectively, based on the evaluation using the kNN classifier.

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Class Center-Based Firefly Algorithm for Handling Missing Data

Cited by 3 publications

References 30 publications

Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

Adaptive multiple imputations of missing values using the class center

Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable

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