TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 100

Data Mining and Knowledge Discovery Handbook, 2 Edition part 100. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 970 Lior Rokach base classifiers present diverse classifications. This arbiter together with an arbitration rule decides on a final classification outcome based upon the base predictions. Figure shows how the final classification is selected based on the classification of two base classifiers and a single arbiter. Instance Final Classification Fig. . A Prediction from Two Base Classifiers and a Single Arbiter. The process of forming the union of data subsets classifying it using a pair of arbiter trees comparing the classifications forming a training set training the arbiter and picking one of the predictions is recursively performed until the root arbiter is formed. Figure illustrate an arbiter tree created for k 4. T T4 are the initial four training datasets from which four classifiers C1 C4 are generated concurrently. T12 and T34 are the training sets generated by the rule selection from which arbiters are produced. A12 and A34 are the two arbiters. Similarly T14 and A14 root arbiter are generated and the arbiter tree is completed. Arbiters Classifiers Data-subsets Ti T2 T3 T4 Fig. . Sample Arbiter Tree. Several schemes for arbiter trees were examined and differentiated from each other by the selection rule used. Here are three versions of rule selection Only instances with classifications that disagree are chosen group 1 . Like group 1 defined above plus instances that their classifications agree but are incorrect group 2 . Like groups 1 and 2 defined above plus instances that have the same correct classifications group 3 . 50 Ensemble Methods in Supervised Learning 971 Two versions of arbitration rules have been implemented each one corresponds to the selection rule used for generating the training data at that level For selection rule 1 and 2 a final classification is made by a majority vote of the classifications of the two lower levels and the arbiter s own classification with preference given to the latter. For selection rule 3 if the .

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