Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems
This paper uses a radial basis function (RBF) transformation of data envelopment analysis (DEA) data to perform RBF-DEA. It is shown that the RBF-DEA frontier identifies cases that have average efficiency scores in traditional DEA. The formal identification of average efficiency cases allows decisio...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827021000463 |
| _version_ | 1819174609471143936 |
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| author | Parag C. Pendharkar |
| author_facet | Parag C. Pendharkar |
| author_sort | Parag C. Pendharkar |
| collection | DOAJ |
| description | This paper uses a radial basis function (RBF) transformation of data envelopment analysis (DEA) data to perform RBF-DEA. It is shown that the RBF-DEA frontier identifies cases that have average efficiency scores in traditional DEA. The formal identification of average efficiency cases allows decision-makers to use these cases and related information for regression, segmentation and cluster analysis. Additionally, negative inputs and outputs can be used in RBF-DEA and unique ranking of fully efficient cases in traditional DEA can be achieved by further evaluating these fully efficient cases against the average RBF-DEA regression frontier. When compared to traditional cluster analysis, RBF-DEA cluster analysis offers unique advantages in that number of clusters do not need to be mentioned and cluster labels are identified by the RBF-DEA technique. Furthermore, unlike the traditional techniques, RBF-DEA cluster memberships are not sensitive to any initial random starting points. |
| first_indexed | 2024-12-22T20:41:42Z |
| format | Article |
| id | doaj.art-36ae057cffbf4a54bb24496ae3c9951a |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-12-22T20:41:42Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-36ae057cffbf4a54bb24496ae3c9951a2022-12-21T18:13:19ZengElsevierMachine Learning with Applications2666-82702021-12-016100092Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problemsParag C. Pendharkar0Information Systems, School of Business Administration, Pennsylvania State University at Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057, United States of AmericaThis paper uses a radial basis function (RBF) transformation of data envelopment analysis (DEA) data to perform RBF-DEA. It is shown that the RBF-DEA frontier identifies cases that have average efficiency scores in traditional DEA. The formal identification of average efficiency cases allows decision-makers to use these cases and related information for regression, segmentation and cluster analysis. Additionally, negative inputs and outputs can be used in RBF-DEA and unique ranking of fully efficient cases in traditional DEA can be achieved by further evaluating these fully efficient cases against the average RBF-DEA regression frontier. When compared to traditional cluster analysis, RBF-DEA cluster analysis offers unique advantages in that number of clusters do not need to be mentioned and cluster labels are identified by the RBF-DEA technique. Furthermore, unlike the traditional techniques, RBF-DEA cluster memberships are not sensitive to any initial random starting points.http://www.sciencedirect.com/science/article/pii/S2666827021000463Radial basis functionsData envelopment analysisCluster analysisRegression analysis |
| spellingShingle | Parag C. Pendharkar Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems Machine Learning with Applications Radial basis functions Data envelopment analysis Cluster analysis Regression analysis |
| title | Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems |
| title_full | Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems |
| title_fullStr | Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems |
| title_full_unstemmed | Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems |
| title_short | Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems |
| title_sort | hybrid radial basis function dea and its applications to regression segmentation and cluster analysis problems |
| topic | Radial basis functions Data envelopment analysis Cluster analysis Regression analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666827021000463 |
| work_keys_str_mv | AT paragcpendharkar hybridradialbasisfunctiondeaanditsapplicationstoregressionsegmentationandclusteranalysisproblems |