Support directional shifting vector: A direction based machine learning classifier
Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32592/1/Support%20directional%20shifting%20vector-a%20direction%20based%20machine%20learning.pdf |
| _version_ | 1825814067233685504 |
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| author | Kowsher, Md. Hossen, Imran Tahabilder, Anik Prottasha, Nusrat Jahan Habib, Kaiser Zafril Rizal, M Azmi |
| author_facet | Kowsher, Md. Hossen, Imran Tahabilder, Anik Prottasha, Nusrat Jahan Habib, Kaiser Zafril Rizal, M Azmi |
| author_sort | Kowsher, Md. |
| collection | UMP |
| description | Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. |
| first_indexed | 2024-03-06T12:53:20Z |
| format | Article |
| id | UMPir32592 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:53:20Z |
| publishDate | 2021 |
| publisher | Ital Publication |
| record_format | dspace |
| spelling | UMPir325922022-01-06T07:46:09Z http://umpir.ump.edu.my/id/eprint/32592/ Support directional shifting vector: A direction based machine learning classifier Kowsher, Md. Hossen, Imran Tahabilder, Anik Prottasha, Nusrat Jahan Habib, Kaiser Zafril Rizal, M Azmi QA76 Computer software T Technology (General) Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Ital Publication 2021-10 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32592/1/Support%20directional%20shifting%20vector-a%20direction%20based%20machine%20learning.pdf Kowsher, Md. and Hossen, Imran and Tahabilder, Anik and Prottasha, Nusrat Jahan and Habib, Kaiser and Zafril Rizal, M Azmi (2021) Support directional shifting vector: A direction based machine learning classifier. Emerging Science Journal, 5 (5). 700 -713. ISSN 2610-9182. (Published) http://dx.doi.org/10.28991/esj-2021-01306 http://dx.doi.org/10.28991/esj-2021-01306 |
| spellingShingle | QA76 Computer software T Technology (General) Kowsher, Md. Hossen, Imran Tahabilder, Anik Prottasha, Nusrat Jahan Habib, Kaiser Zafril Rizal, M Azmi Support directional shifting vector: A direction based machine learning classifier |
| title | Support directional shifting vector: A direction based machine learning classifier |
| title_full | Support directional shifting vector: A direction based machine learning classifier |
| title_fullStr | Support directional shifting vector: A direction based machine learning classifier |
| title_full_unstemmed | Support directional shifting vector: A direction based machine learning classifier |
| title_short | Support directional shifting vector: A direction based machine learning classifier |
| title_sort | support directional shifting vector a direction based machine learning classifier |
| topic | QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/32592/1/Support%20directional%20shifting%20vector-a%20direction%20based%20machine%20learning.pdf |
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