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classifier benefits over classifier

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classifier benefits over classifier
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Machine Learning Classifiers. What is classification? 2018611ensp;0183;ensp;A classifier utilizes some training data to understand how given input variables relate to the class. In this case, known spam and nonspam emails have to be used as the training data. When the classifier Multiple classifier architectures and their application 2011416ensp;0183;ensp;Adaboost did provide statistically signif

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    200811ensp;0183;ensp;Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult realworld problems.

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  • Phishing detection using classifier ensembles IEEE

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  • Multiple classifier architectures and their application

    2011416ensp;0183;ensp;Adaboost did provide statistically significant improvements in performance over the single best classifier in a number of experiments, but was relatively poor compared to the best multistage and static parallel methods. This concurs with previous comparisons of bagging and boosting applied to credit scoring (West et al., 2005). Dynamic classifier systems, that look to segment the population into a

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    2000915ensp;0183;ensp;Current research has shifted to the use of accuracybased fitness. This paper presents a simple kov model of the algorithm in such systems, allowing comparison between the two forms of rule utility measure. Using a singlestep task the previously discussed benefits of accuracy over prediction are clearly shown with regard to overgeneral rules.

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