Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures
Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/23/3929 |
| _version_ | 1797463416375345152 |
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| author | Faten Khalid Karim Hela Elmannai Abdelrahman Seleem Safwat Hamad Samih M. Mostafa |
| author_facet | Faten Khalid Karim Hela Elmannai Abdelrahman Seleem Safwat Hamad Samih M. Mostafa |
| author_sort | Faten Khalid Karim |
| collection | DOAJ |
| description | Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R<sup>2</sup> score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods. |
| first_indexed | 2024-03-09T17:50:23Z |
| format | Article |
| id | doaj.art-6f8bf6e733574dd59cb45938c6ec4693 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:50:23Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6f8bf6e733574dd59cb45938c6ec46932023-11-24T10:47:52ZengMDPI AGElectronics2079-92922022-11-011123392910.3390/electronics11233929Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy MeasuresFaten Khalid Karim0Hela Elmannai1Abdelrahman Seleem2Safwat Hamad3Samih M. Mostafa4Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaComputer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, EgyptScientific Computing Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptComputer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, EgyptHandling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R<sup>2</sup> score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods.https://www.mdpi.com/2079-9292/11/23/3929missingness mechanismsfeature selectionbayesian ridge regressionimputationsimilarity classifier |
| spellingShingle | Faten Khalid Karim Hela Elmannai Abdelrahman Seleem Safwat Hamad Samih M. Mostafa Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures Electronics missingness mechanisms feature selection bayesian ridge regression imputation similarity classifier |
| title | Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures |
| title_full | Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures |
| title_fullStr | Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures |
| title_full_unstemmed | Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures |
| title_short | Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures |
| title_sort | handling missing values based on similarity classifiers and fuzzy entropy measures |
| topic | missingness mechanisms feature selection bayesian ridge regression imputation similarity classifier |
| url | https://www.mdpi.com/2079-9292/11/23/3929 |
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