Universiti Teknologi Malaysia Institutional Repository

Using rough set through for classification of image segmentation data

Diallo Abdoulaye, Kindy (2014) Using rough set through for classification of image segmentation data. Masters thesis, Universiti Teknologi Malaysia, Advanced Informatics School.

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Abstract

Knowledge Discovery in Database (KDD) can be defined as a technology or a process that helps to extract valuable information including hidden and unseen patterns, trends and relationships between variables from a large amount of data. The information learnt and the discovery made can help in applying the new found pattern in the training set to an unseen data, known as test set, that can guide and facilitate a crucial business decision making task. A large number of data mining techniques have been proposed in the literature for classification purpose. In this work, we are using the Rough Set Classifier (RSC) for mining image segmentation data set obtained from an online machine learning data repository. The RSC is a rule based data mining technique which generates rules from large databases and has great capabilities to deal with noise and uncertainty in data set. In order to find out the best accuracy method, we conducted around 10 experiments by varying the proportions between the training and test sets. The best method gave us an accuracy of 85.71%.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains (Jaminan Maklumat)) - Universiti Teknologi Malaysia, 2014
Subjects:Q Science > QA Mathematics
Divisions:Advanced Informatics School
ID Code:48667
Deposited By: Haliza Zainal
Deposited On:15 Oct 2015 01:09
Last Modified:16 Jun 2020 08:00

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