A Robust Ensemble based Approach to Combine Heterogeneous Classifiers in the Presence of Class Label Noise

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dc.contributor.author Dr Shehzad Khalid
dc.date.accessioned 2017-11-22T06:58:59Z
dc.date.available 2017-11-22T06:58:59Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/123456789/5005
dc.description.abstract In this paper, we introduced a classifier ensemble approach to combine heterogeneous classifiers in the presence of class label noise in the datasets. To enhance the performance of classifier ensemble, we give a preprocessing approach to filter out this class label noise. The filtered data is then used to learn individual classifier model. After that, a weight learning method is introduced to learn weights on each individual classifier to create a classifier ensemble. We applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give best accuracy. The proposed approach is evaluated on variety of real life datasets. The proposed technique is also compared with existing standard ensemble techniques such as Adaboost, Bagging and RSM to show the superiority of proposed ensemble method, in the presence of class label noise, as compared to its competitors and also to show the sensitivity of competitors to class label noise. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.subject Department of Computer Engineering CE en_US
dc.title A Robust Ensemble based Approach to Combine Heterogeneous Classifiers in the Presence of Class Label Noise en_US
dc.type Article en_US


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