CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio

Mostafa, Samih M.; Eladimy, Abdelrahman S.; Hamad, Safwat; Amano, Hirofumi

doi:10.1109/access.2020.3042119

Cited by 22 publications

(15 citation statements)

References 37 publications

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“…But such a solution may result in bad analysis, losing the ability to recognize statistically significant variations and may also generates bias. Missingness mechanisms have a large effect on FS that's why before applying any FS technique missingness mechanisms need to be taken into consideration [3].…”

Section: Feature Selectionmentioning

confidence: 99%

“…In single imputation, MVs are imputed by a value one time. Though, single imputation does not require computational resources it can result in biased results [3]. In multiple imputation, m copies from the original dataset are generated.…”

Section: Handling Missing Datamentioning

confidence: 99%

“…Machine learning (ML), big data and any data driven tool require high data quality which results in good analysis and outcomes. The existence of MVs within a dataset can result in problems, for instance, bad data analysis, reducing the research results obtained from such dataset and presenting amount of bias [3]. To this end, significant information is incorporated within MVs which should be manipulated before using the incomplete dataset with any data driven tool.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Algarni¹,

Ragab²,

Alamri³

et al. 2022

Computer Systems Science and Engineering

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The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset's values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash's fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Handling Missing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Algarni¹,

Ragab²,

Alamri³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…These widely used FS methods are classified into four categories: (1) Filter-based methods evaluate features depending on their inherent properties, such as the statistical properties of the data. The most popular filter methods are chisquare [12], the gain ratio [13], information gain [14], ReliefF [15] and minimum Redundancy Maximum Relevance (mRMR) [16]. The main advantages of filter methods are that they are not dependent on classifiers and are fast and straightforward in terms of computation.…”

Section: Introductionmentioning

confidence: 99%

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Huang

Wang

et al. 2022

Entropy

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Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

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“…Academics have relied on a variety of ad hoc methods to "repair" data for decades, such as eliminating incomplete instances or replacing missing values. A few missing worth attribution approaches execution relies upon the size of the dataset and the quantity of missing qualities inside the dataset [7]. Unfortunately, the bulk of these solutions are prone to severe bias since they rely on a relatively rigid assumption about the cause of missing data.…”

Section: Introductionmentioning

confidence: 99%

A novel optimization algorithm for the missing data in HCC based on multiple imputation and genetic algorithm

Salah¹,

Hammad²,

Abdelkader

2021

IJCI. International Journal of Computers and Information

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Hepatocellular carcinoma (HCC) is a threat to the liver, which is considered one of the diseases devastating to human health that leads to death. Therefore, discovering HCC early is essential, this will not begin without complete, adequate, and reliable data. Hence, it is imperative to improve missing data completion processes to provide more reliable data in the detection phase. In this research, we offer a unique method that combines multiple imputations with a genetic algorithm to optimize multiple regression imputation processes and obtain the optimum fitness values for missing data from patients. We used 583 patient records from a public, available database to train and evaluate our proposed algorithm, separated into 416 liver patient records and 167 non-liver patient records. Results are proven that the proposed approach has the most improvement for missing data results. We were able to reach the optimal value which was measured by fitness value to 233 instead of using the normal equation in multiple imputations which gave 92.72 as the uttermost fitness value of it. The suggested model may be validated using a large database and used in HCC laboratories to assist doctors in making an accurate diagnosis.

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CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio

Cited by 22 publications

References 37 publications

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

A novel optimization algorithm for the missing data in HCC based on multiple imputation and genetic algorithm

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