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Increasing the Accuracy of Predictive Algorithms: A Review of Ensembles of Classifiers

Increasing the Accuracy of Predictive Algorithms: A Review of Ensembles of Classifiers
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Author(s): Sotiris Kotsiantis (University of Patras, Greece & University of Peloponnese, Greece), Dimitris Kanellopoulos (University of Patras, Greece) and Panayotis Pintelas (University of Patras, Greece & University of Peloponnese, Greece)
Copyright: 2009
Pages: 5
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch300

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Abstract

In classification learning, the learning scheme is presented with a set of classified examples from which it is expected tone can learn a way of classifying unseen examples (see Table 1). Formally, the problem can be stated as follows: Given training data {(x1, y1)…(xn, yn)}, produce a classifier h: X- >Y that maps an object x ? X to its classification label y ? Y. A large number of classification techniques have been developed based on artificial intelligence (logic-based techniques, perception-based techniques) and statistics (Bayesian networks, instance-based techniques). No single learning algorithm can uniformly outperform other algorithms over all data sets. The concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual machine learning algorithms. Numerous methods have been suggested for the creation of ensembles of classi- fiers (Dietterich, 2000). Although, or perhaps because, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best.

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