Classification Trees with Synthetic Features for Multiclass Classification Problems
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics
Date of Award
Spring 2022
Abstract
In data mining, classification is considered one of the problems under supervised machine learning. One popular method for solving classification problems is the classification tree. Most of the algorithms for classification tree are used for binary classifiers. We throw more light on binary classification in this work and extend the knowledge into a multiclass classification problems. The two main approaches for dealing with multiclass classifications are reducing the multiclass classification problem to a collection of binary classification problems and directly boosting the multiclass classification problems. We give notes on the concepts of multiclass classification problems, throw more light on the different methods that are under the two main approaches, use the first method to conduct experiments with reliable datasets, then use the concept of cross-validation to fit, test and compare the accuracy of different models. We make use of original scripts in R and use concepts such as variable importance, and voting when fitting model and making predictions.
Advisor
Thomas Boucher
Subject Categories
Applied Mathematics | Mathematics | Physical Sciences and Mathematics
Recommended Citation
Owusu, Paul Junior, "Classification Trees with Synthetic Features for Multiclass Classification Problems" (2022). Electronic Theses & Dissertations. 744.
https://digitalcommons.tamuc.edu/etd/744
