Download Pattern Classification Using Ensemble Methods by Lior Rokach PDF

By Lior Rokach

Researchers from numerous disciplines corresponding to trend attractiveness, records, and computer studying have explored using ensemble technique because the past due seventies. therefore, they're confronted with a large choice of tools, given the becoming curiosity within the box. This publication goals to impose a level of order upon this range by means of offering a coherent and unified repository of ensemble equipment, theories, traits, demanding situations and functions. The ebook describes intimately the classical tools, in addition to the extensions and novel techniques constructed lately. besides algorithmic descriptions of every process, it additionally explains the conditions during which this technique is appropriate and the results and the trade-offs incurred by utilizing the strategy.

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2004)]. 7. The algorithm assumes that the training set consists of m instances, which are either labeled as −1 or +1. The classification of a new instance is obtained by voting on all classifiers {Mt }, each having an overall accuracy of αt . 1) t=1 Breiman [Breiman (1998)] explores a simpler algorithm called Arcx4 whose purpose it to demonstrate that AdaBoost works not because of the specific form of the weighing function, but because of the adaptive 30 Pattern Classification Using Ensemble Methods AdaBoost Training Require: I (a weak inducer), T (the number of iterations), S (training set) Ensure: Mt , αt ; t = 1, .

7 The AdaBoost algorithm. resampling. 2) where mti is the number of misclassifications of the i-th instance by the first t classifiers. AdaBoost assumes that the weak inducers, which are used to construct the classifiers, can handle weighted instances. For example, most decision tree algorithms can handle weighted instances. However, if this is not the case, an unweighted dataset is generated from the weighted data via resampling. Namely, instances are chosen with a probability according to their weights (until the dataset becomes as large as the original training set).

The idea is to construct an intermediate learner operating on the combined linear and quadratic terms. First a classifier is trained by randomizing the labels of the training 46 Pattern Classification Using Ensemble Methods examples. Next, the learning algorithm is called repeatedly, using a systematic update of the labels of the training examples in each round. This method is in contrast to the AdaBoost algorithm that uses reweighting of training examples. Together they form a powerful combination that makes intensive use the given base learner by both reweighting and relabeling the original training set.

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