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dc.contributor.advisor Abdelbar, Ashraf
dc.contributor.author Abdel Salam, Ismail Mohamed Anwar
dc.date.accessioned 2015-05-24T13:55:16Z
dc.date.available 2015-05-24T22:00:09Z
dc.date.created 2015 Spring en_US
dc.date.issued 2015-05-24
dc.identifier.uri http://dar.aucegypt.edu/handle/10526/4357
dc.description.abstract Classi cation is a central problem in the elds of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classi er) that can be used to predict the class of new unlabeled instances. Data preparation is crucial to the data mining process, and its focus is to improve the tness of the training data for the learning algorithms to produce more e ective classi ers. Two widely applied data preparation methods are feature selection and instance selection, which fall under the umbrella of data reduction. For my research I propose ADR-Miner, a novel data reduction algorithm that utilizes ant colony optimization (ACO). ADR-Miner is designed to perform instance selection to improve the predictive e ectiveness of the constructed classi cation models. Two versions of ADR-Miner are developed: a base version that uses a single classi cation algorithm during both training and testing, and an extended version which uses separate classi cation algorithms for each phase. The base version of the ADR-Miner algorithm is evaluated against 20 data sets using three classi cation algorithms, and the results are compared to a benchmark data reduction algorithm. The non-parametric Wilcoxon signed-ranks test will is employed to gauge the statistical signi cance of the results obtained. The extended version of ADR-Miner is evaluated against 37 data sets using pairings from fi ve classi cation algorithms and these results are benchmarked against the performance of the classi cation algorithms but without reduction applied as pre-processing. Keywords: Ant Colony Optimization (ACO), Data Mining, Classi cation, Data Reduction. en_US
dc.format.extent 129 p. en_US
dc.format.medium theses en_US
dc.language.iso en en_US
dc.rights Author retains all rights with regard to copyright. en
dc.subject Ant Colony Optimization (ACO) en_US
dc.subject Data Mining en_US
dc.subject Classification en_US
dc.subject Data Reduction en_US
dc.subject.lcsh Thesis (M.S.)--American University in Cairo en_US
dc.subject.lcsh Artificial intelligence.
dc.subject.lcsh Data mining.
dc.subject.lcsh Data reduction -- Computer programs.
dc.subject.lcsh Computational complexity.
dc.subject.lcsh Electronic data processing.
dc.subject.lcsh Automatic classification.
dc.title ADR-Miner: An Ant-based data reduction algorithm for classification en_US
dc.type Text en_US
dc.subject.discipline Computer Science en_US
dc.rights.access This item is available en_US
dc.contributor.department American University in Cairo. Dept. of Computer Science and Engineering en_US
dc.description.irb American University in Cairo Institutional Review Board approval has been obtained for this item. en_US
dc.contributor.committeeMember Goneid, Amr
dc.contributor.committeeMember Ismail, Ismail Amr


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  • Theses and Dissertations [1728]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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