Pattern Classification Using Ensemble MethodsWorld Scientific, 30. nov 2009 - 244 pages Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method. |
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according accuracy AdaBoost applied approach arbiter Artificial Intelligence average bagging base classifiers base inducer Bayesian binary classifiers boosting algorithm Breiman clas class label clustering code-matrix codeword columns constructed cross-validation Data Mining databases dataset Decision Stump decision tree decomposition defined Dietterich distribution dom(y domains ECOC ensemble methods error rate estimation evaluation feature selection feature subsets function genetic algorithms Hadamard matrix Hamming distance IEEE Transactions improve induction algorithm input attributes instance space International Conference Iris Iris-setosa K—means Knowledge Discovery Kohavi learner learning algorithm Machine Learning matrix misclassified Na¨ıve Bayes neural networks node number of instances number of iterations obtained optimal original training set output codes overfitting partitioning Pattern Recognition performance positive prediction probability Proceedings Qrecall Random Forest randomly RapidMiner resampling Rokach sample Schapire statistical strategy Support Vector Machines target attribute technique test set training data training error training set tuples variance vector voting weight yes full