SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

Dr. Sami Ahmed Haider · Dr. Gargishankar Verma · Arijeet Chandra · Dr. Haewon Byeon

2023年12月 · Xoffencerpublication

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The subset of machine learning algorithms known as supervised learning is an essential component that makes a substantial contribution to the resolution of a wide variety of problems that are associated with the study of artificial intelligence (AI). A dataset that has been labeled is given to the algorithm during the supervised learning phase. This dataset contains not only the input data but also the target labels that correlate to those data. Both sets of information are included. The objective of this activity is to construct a model or a mapping that is able to reliably predict the labels for data that has not yet been observed. There are a large number of algorithms that are commonly used for supervised learning, and each of these techniques has a number of benefits as well as some drawbacks. The technique known as linear regression, which is applied in situations involving continuous numerical data, is one method that is frequently used. Creating a linear link between the input features and the variable that you want to change is the method that is used to accomplish this goal. Logistic regression is often utilized when the objective is to categorize individual data points into a number of separate groups or classes. It constructs a model that calculates the probability that a certain data point belongs to a particular category. Decision trees are a type of general-purpose algorithm that can be put to use for a variety of different classification and regression-related projects. They do this by constructing a tree-like structure, where each leaf node represents a projected class or value and each inside node represents a decision that was taken based on a feature. In other words, each node in the structure represents a decision that was made. The performance of prediction tasks can be improved using ensemble methods such as Random Forests and Gradient Boosting. These methods work by combining many decision trees into a single model. They are especially useful when it comes to managing difficult datasets. Support Vector Machines, often known as SVMs, are useful tools for binary classification because they pinpoint the hyperplane that achieves the optimal margin between classes. Because of this, they are able to deliver satisfactory results whenever there is a noticeable divide between the classes.

作者简介

Sami Ahmed Haider is working as an Assistant Professor at the University of Glasgow, currently placed at Glasgow College, UESTC, Chengdu. Before that, he worked as a senior lecturer at the University of Worcester, UK, in the computing department from 2017-2022. He received his Ph.D. in Information Science and Communication Engineering from Zhejiang University, Hangzhou, China. Dr. Sami has some industry and academia combined experience with expertise in Signal Processing, Wireless Communication and Networks and Artificial Intelligence (Machine and Deep Learning). He is also a co-founder and research consultant in Asia for Synapsify Ltd. Pakistan. He has authored and co-authored several international journals, research articles, and conferences and has a few patents credited to his name. Dr. Sami is also an associate editor of IEEE Transaction on Intelligent Transportation Systems and a reviewer of several good impact factor IEEE, Elsevier and Springer journals.

Dr. Gargishankar Verma Received Ph.D. from C M J University Meghalaya, in the Computer Science Engineering. Currently he is working in Columbia Institute of Engineering and Technology Raipur as Associate Professor in the Department of Computer Science and Engineering Department. He has more than 20 years of experience in teaching and research.

Arijeet Chandra has MTech (Software Systems), MSc, B.Tech. & having over 17 years’ professional work experience in a wide gamut of functional areas of Communication Technologies, Information Technology, Critical Infrastructure Development, Risk assessment and management, Cyber Security, AI and other emerging niche technologies. Currently working with Government of India and holding multiple Indian and foreign pending patents. A keen researcher with sound exposure to emerging trends and happenings in the Cyber Security, AI and Data Science Fields

Dr. Haewon Byeon Received the Dr Sc degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on A 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books