ADABoost (Adaptive Boosting) is a meta-algorithm used to improve classification results. The concept is to make a lot of weak classifiers cooperate to boost results. Adaptability means in this case that detection of the wrong classification makes the algorithm do more work on it (by changing the wages and setting algorithm to do more effort where it failed).
AdaBoost is sensitive to noisy data or outliers.
[http://www.cs.princeton.edu/~schapire/boost.html; Wu i inni “Top 10 algorithms in data mining” Springer 2008]
admin November 19th, 2016