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dc.contributor.authorUmmul Khir, Ramli
dc.date.accessioned2016-06-17T07:23:21Z
dc.date.available2016-06-17T07:23:21Z
dc.date.issued2015-06
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/42093
dc.descriptionAccess is limited to UniMAP community.en_US
dc.description.abstractSince most real-world applications of classification learning involve continuous-valued attributes, extracting data pattern from raw data is an important task. The major purpose of this project is to build a discretization algorithm using boundary cut-points technique known as Entropy-based Discretization According to Distribution of Boundary Point(EDA-DB) Technique to extract essential features. Boundary cut point is a cut point involving examples of different classes. A cut point is defined as the midpoint between each successive pair of values in the sorted sequence of attribute values. The project is developed using Matlab, WEKA, Decision Stump classifier and Random Forest classifier via endoscopic gastritis data set. EDA-DB selected minimum entropy boundary cut point that is spread out within an interval. As a result of discretization process, good generalized data patterns of Endoscopic Gastritis are generated. On top of that essential features are also produced. Thus, determining discretized data pattern from the extracted Endoscopic Gastritis features may improve the overall classification process.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subjectEssential featuresen_US
dc.subjectEndoscopic gastritis dataen_US
dc.subjectGastritisen_US
dc.subjectDiscretization processen_US
dc.titleEDA-DB discretization method to extract essential features for endoscopic gastritis dataseten_US
dc.typeLearning Objecten_US
dc.contributor.advisorYasmin Mohd Yacoben_US
dc.publisher.departmentSchool of Computer and Communication Engineeringen_US


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