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They implement a strong classifier by integrating many weak classifiers.
The main idea is to combine the performance of many weak classifiers to produce a powerful strong classifier.
Too many weak classifiers in the system sometimes increase its complexity and computational consumption to intolerable level.
The key idea is to find many weak classifiers and combine them in a proper way deriving a single strong classifier.
The Random Forest classification algorithm is extensively used in ~omics data analysis because it is less resource-intensive than many classification algorithms (e.g.: Bayesian algorithms), it makes no implicit assumption regarding data properties and it is specifically suited to deal with the small n large p problem due to the use of many weak classifiers (see below).
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Rather than using all weak classifiers, we propose to use only the weak classifiers that should classify the upper part of the face (in green in the figure).
Weak classifiers voting.
Weak classifiers formed a committee.
These weak classifiers are used to construct a strong classifier,.
Thus, non-biased weak classifiers are desired.
Creating the McCascade using available weak classifiers.
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