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Incremental learning with ensemble based SVM classifiers for non-stationary environments

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dc.contributor Graduate Program in Computer Engineering.
dc.contributor.advisor Gürgen, Fikret.
dc.contributor.author Yalçın, Aycan.
dc.date.accessioned 2023-03-16T10:05:50Z
dc.date.available 2023-03-16T10:05:50Z
dc.date.issued 2006.
dc.identifier.other CMPE 2006 Y35
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12477
dc.description.abstract In this thesis, we evaluate the performance of support vector machines (SVM) ensemble, which is constructed by using Learn++ algorithm, on changing environment and propose incorporating forgetting mechanism to adapt this algorithm to changing environment. In most of the real world applications, the data is collected over an extended period of time and the distribution underlying the data is likely to change by time. These changes make the model built on old data inconsistent with the new data, and regular updating of the model is necessary. For effective learning in a changing environment, the algorithm should be able to detect context change and quickly adjust the hypothesis to the current context. This can be achieved by revising the model by incorporating new examples and eliminating the effect of outdated concepts. Learn++ is an ensemble based incremental learning algorithm that is able to learn new information. Therefore, it can be easily adapted to changing environments by using a forgetting mechanism to remove the redundant data from the ensemble. In this thesis, we propose using a forgetting strategy that is based on the performance of the base classifiers. Only the best K classifiers or the classifiers whose classification performance exceeds a threshold value are kept in the ensemble. Our results indicate that incorporating forgetting mechanism improves the classification performance of the proposed algorithm on a changing environment. The proposed algorithm can effectively handle the gradual changes.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2006.
dc.subject.lcsh Machine learning.
dc.subject.lcsh Algorithms.
dc.title Incremental learning with ensemble based SVM classifiers for non-stationary environments
dc.format.pages xi, 54 leaves;


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