SDSV

Abstract

Data classification is one of the most fundamental tasks that can be accomplished by supervised machine learning. There exists a lot of algorithms, and they have the specific case of uses. Different classification methods follow different techniques to map the relationship between input and output. This article proposes an angle measurement-based classification technique called Support Directional Shifting Vectors (SDSV) to segment a spectral domain into regions with a very effective solution for classification problems. This method introduces two shifting vectors, named Support Direction Vector (SDV) and Support Origin Vector (SOV). These vectors are formed as a linear function to measure cosine-angle by the dot product of two separated data classes, named target data points and non-target data points. Considering the target class samples, the vectors get aligned in a way that the angle with the target class gets minimized, while the angle with the non-target class gets maximized. The error in the position of the linear function has been modeled as the loss function. Then, the vector position is updated iteratively by optimizing this loss function using a gradient descent algorithm. We have used this model to classify data from two different machine learning datasets to evaluate the performance of the proposed method. Finally, we have carefully examined the results and compared them with the other standard classification algorithms. In summary, the proposed SDSV algorithm has shown a comparable accuracy compared to different standard algorithms.