Research Article
Analysis of Supervised Machine Learning Classifier Techniques used in Gesture Classification
@INPROCEEDINGS{10.4108/eai.16-4-2022.2318147, author={Ashutosh Mohite and Akhilesh A. Waoo}, title={Analysis of Supervised Machine Learning Classifier Techniques used in Gesture Classification}, proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India}, publisher={EAI}, proceedings_a={THEETAS}, year={2022}, month={6}, keywords={human-computer interaction (hci) machine learning classification decision tree support vector machine (svm) multilayer perceptron (mlp) na\~{n}ve bayes}, doi={10.4108/eai.16-4-2022.2318147} }- Ashutosh Mohite
Akhilesh A. Waoo
Year: 2022
Analysis of Supervised Machine Learning Classifier Techniques used in Gesture Classification
THEETAS
EAI
DOI: 10.4108/eai.16-4-2022.2318147
Abstract
In the digital era, in the field of computer science to perform any task based on complex data, we need to arrange and pre-process that data because real-world data may be noisy, complex, and unclassified. Therefore, to make our task easy and to get accurate results, we must classify our data. Classification plays a major role in Human-Computer Interaction (HCI). In HCI Hand Gesture is a broadly used method for interaction with the system. In the classification of gesture data in machine learning, different classifiers like Naïve Bayes, KNN, Decision Tree, Support Vector Machine (SVM), Multilayer Perceptron (MLP), etc. are used. In this study, we compare the different classifiers and concentrated on the central principles of each technique and its advantages and drawbacks.