Support directional shifting vector: A direction based machine learning classifier

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)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Supervised machine learning; Classification; Cosine similarity; Directional vectors; Angle measurement
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Faculty/Division: Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 06 Jan 2022 07:46
Last Modified: 06 Jan 2022 07:46
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