Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Emerging Science Journal
Zafril Rizal M. Azmi
Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang,
Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Emerging Science Journal

 1 
Support Directional Shifting Vector: A Direction Based Machine Learning Classifier Md. Kowsher; Imran Hossen; Anik Tahabilder; Nusrat Jahan Prottasha; Kaiser Habib; Zafril Rizal M. Azmi
Emerging Science Journal Vol 5, No 5 (2021): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-01306

Abstract

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. Doi: 10.28991/esj-2021-01306 Full Text: PDF
Co-Authors Anik Tahabilder Imran Hossen Kaiser Habib Md. Kowsher Nusrat Jahan Prottasha