An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem
This study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/11/2050 |
| _version_ | 1797457714938380288 |
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| author | Jelena Mišić Aleksandar Kemiveš Milan Ranđelović Dragan Ranđelović |
| author_facet | Jelena Mišić Aleksandar Kemiveš Milan Ranđelović Dragan Ranđelović |
| author_sort | Jelena Mišić |
| collection | DOAJ |
| description | This study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any process of binary classification. In addition, an ensemble stacking model that involves the asymmetric use of two different well-known algorithms is proposed to determine the importance of individual factors. This model is constructed so that the standard logistic regression is first applied as mandatory. Further, the classification algorithms are implemented if the defined conditions are met. Finally, feature selection algorithms, which belong to the optimization group of algorithms, are applied as a combinatorial algorithm. The proposed model is verified through a case study conducted using real data obtained from health institutions in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model can achieve better results than each of the methods included in it and surpasses several state-of-the-art ensemble algorithms in the field of machine learning. The proposed solution has been implemented in the form of a modern mobile application. |
| first_indexed | 2024-03-09T16:25:00Z |
| format | Article |
| id | doaj.art-d36a396ada5d42b8be9b80f4b4d368bb |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T16:25:00Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-d36a396ada5d42b8be9b80f4b4d368bb2023-11-24T15:08:54ZengMDPI AGSymmetry2073-89942023-11-011511205010.3390/sym15112050An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate ProblemJelena Mišić0Aleksandar Kemiveš1Milan Ranđelović2Dragan Ranđelović3Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, SerbiaDepartment for Postgraduate Studies, Singidunum University, Danijelova 32, 11000 Belgrade, SerbiaScience Technology Park Niš, Aleksandra Medvedeva 2a, 18000 Niš, SerbiaFaculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, Travnička 2, 11000 Belgrade, SerbiaThis study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any process of binary classification. In addition, an ensemble stacking model that involves the asymmetric use of two different well-known algorithms is proposed to determine the importance of individual factors. This model is constructed so that the standard logistic regression is first applied as mandatory. Further, the classification algorithms are implemented if the defined conditions are met. Finally, feature selection algorithms, which belong to the optimization group of algorithms, are applied as a combinatorial algorithm. The proposed model is verified through a case study conducted using real data obtained from health institutions in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model can achieve better results than each of the methods included in it and surpasses several state-of-the-art ensemble algorithms in the field of machine learning. The proposed solution has been implemented in the form of a modern mobile application.https://www.mdpi.com/2073-8994/15/11/2050binary classification algorithmlogistic regressionfeature selectionensemble methodfactors for successful inpatient treatment |
| spellingShingle | Jelena Mišić Aleksandar Kemiveš Milan Ranđelović Dragan Ranđelović An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem Symmetry binary classification algorithm logistic regression feature selection ensemble method factors for successful inpatient treatment |
| title | An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem |
| title_full | An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem |
| title_fullStr | An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem |
| title_full_unstemmed | An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem |
| title_short | An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem |
| title_sort | asymmetric ensemble method for determining the importance of individual factors of a univariate problem |
| topic | binary classification algorithm logistic regression feature selection ensemble method factors for successful inpatient treatment |
| url | https://www.mdpi.com/2073-8994/15/11/2050 |
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