Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78792
Title: Random forest for image classification
Authors: Yong, Choi Chin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: Random forest is a popular machine learning algorithm which is made up of an ensemble of decision trees. The advancements in machine learning techniques have been made possible by advances in technology due to globalisation. Image classification, on the other hand, refers to the introduction of an input image and returning the output of a class or a probability of classes that best describes the image. It is known to be a broad topic, such that the revolution in image classification methods have been made possible by recent advancements in computer technology. This report illustrates the practical work done over the academic year with regards to random forest and image classification, about how the accuracy of a single decision tree compares to that of an ensemble of decision trees, as well as how the random forest model increases in accuracy with the increase in number of decision trees used in experimentation.
URI: http://hdl.handle.net/10356/78792
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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