Classification model with collinear grouping of features
ABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in pr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2022.2129133 |
| _version_ | 1811163591216201728 |
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| author | Leon Bobrowski Paweł Zabielski |
| author_facet | Leon Bobrowski Paweł Zabielski |
| author_sort | Leon Bobrowski |
| collection | DOAJ |
| description | ABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in practice, for example in genetics. The design of classification or prognostic models on small data sets requires the development of new types of methods. Methods based on L1 geometry can play an important role in this regard. |
| first_indexed | 2024-04-10T15:07:48Z |
| format | Article |
| id | doaj.art-b93a0f59b6384eeca7026ab075fa137c |
| institution | Directory Open Access Journal |
| issn | 2475-1839 2475-1847 |
| language | English |
| last_indexed | 2024-04-10T15:07:48Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj.art-b93a0f59b6384eeca7026ab075fa137c2023-02-14T20:06:26ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472023-01-0171738810.1080/24751839.2022.2129133Classification model with collinear grouping of featuresLeon Bobrowski0Paweł Zabielski1Faculty of Computer Science, Bialystok University of Technology, Bialystok, PolandFaculty of Computer Science, Bialystok University of Technology, Bialystok, PolandABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in practice, for example in genetics. The design of classification or prognostic models on small data sets requires the development of new types of methods. Methods based on L1 geometry can play an important role in this regard.https://www.tandfonline.com/doi/10.1080/24751839.2022.2129133Small data setsmultidimensional feature vectorsmaximizing L1 norm marginsconvex and piecewise linear criterion functions |
| spellingShingle | Leon Bobrowski Paweł Zabielski Classification model with collinear grouping of features Journal of Information and Telecommunication Small data sets multidimensional feature vectors maximizing L1 norm margins convex and piecewise linear criterion functions |
| title | Classification model with collinear grouping of features |
| title_full | Classification model with collinear grouping of features |
| title_fullStr | Classification model with collinear grouping of features |
| title_full_unstemmed | Classification model with collinear grouping of features |
| title_short | Classification model with collinear grouping of features |
| title_sort | classification model with collinear grouping of features |
| topic | Small data sets multidimensional feature vectors maximizing L1 norm margins convex and piecewise linear criterion functions |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2022.2129133 |
| work_keys_str_mv | AT leonbobrowski classificationmodelwithcollineargroupingoffeatures AT pawełzabielski classificationmodelwithcollineargroupingoffeatures |