Ensemble Learning
نویسنده
چکیده
An ensemble contains a number of learners which are usually called base learners. The generalization ability of an ensemble is usually much stronger than that of base learners. Actually, ensemble learning is appealing because that it is able to boost weak learners which are slightly better than random guess to strong learners which can make very accurate predictions. So, “base learners” are also referred as “weak learners”. It is noteworthy, however, that although most theoretical analyses work on weak learners, base learners used in practice are not necessarily weak since using not-so-weak base learners often results in better performance. Base learners are usually generated from training data by a base learning algorithm which can be decision tree, neural network or other kinds of machine learning algorithms. Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, but there are also some methods which use multiple learning algorithms to produce heterogeneous learners. In the latter case there is no single base learning algorithm and thus, some people prefer calling the learners individual learners or component learners to “base learners”, while the names “individual learners” and “component learners” can also be used for homogeneous base learners. It is difficult to trace the starting point of the history of ensemble methods since the basic idea of deploying multiple models has been in use for a long time, yet it is clear that the hot wave of research on ensemble learning since the 1990s owes much to two works. The first is an applied research conducted by Hansen and Salamon [1] at the end of 1980s, where they found that predictions made by the combination of a set of classifiers are often more accurate than predictions made by the best single classifier. The second is a theoretical research conducted in 1989, where Schapire [2] proved that weak learners can be boosted to strong learners, and the proof resulted in Boosting, one of the most influential ensemble methods.
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