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Optimizing Data Mining Algorithms for Classification Tasks

This research focuses on preprocessing data, feature selection methods, and classifier optimization in data mining, with a detailed exploration of algorithms such as BestFirst, Genetic Search, and classification techniques like Bagging and Decorate. The study culminates in achieving a final error rate of 3.50%.

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Optimizing Data Mining Algorithms for Classification Tasks

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  1. Data mining Μιχαηλίδου Χριστίνα Μάιος 2009

  2. προεπεξεργασια • Μετατροπή της output σε nominal • Επιλογή χαρακτηριστικών • BestFirst – CfsSubSetEval6 attr. • Genetic Search – CfsSubsetEval 14 attr.

  3. classify • BestFirst – CfsSubSetEval (attributes 5, 6, 18, 26, 34, 36) ΑΛΓΟΡΙΘΜΟΙ:oneR, conjuctiveRule, BayesNet, MetaAdaBoost, MetaEnd.

  4. classify • Genetic Search – CfsSubsetEval (attributes 1,4,5,6,13,14,18,20,26,29,30,32,33,37)

  5. Preprocess • filtersinstanceRemoveMissClassified • Classifier: Decorate classify • classifiersmetaBagging • Σφάλμα: 4.23% • classifiersmetaΕnd • Σφάλμα: 3.50% (Τελική επιλογή)

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