Classic AdaBoost Classifier

Weak threshold classifier boosted to strong Classifier with Adaboost
Updated 20 Jan 2012

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This a classic AdaBoost implementation, in one single file with easy understandable code.

The function consist of two parts a simple weak classifier and a boosting part:
The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1
The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier"
Training mode:
Apply mode:

datafeatures : An Array with size number_samples x number_features
dataclass : An array with the class off all examples, the class
can be -1 or 1
itt : The number of training iterations
model : A struct with the cascade of weak-classifiers
estimateclass : The by the adaboost model classified data

Please leave a comment, if you like the code, find a bug or have a suggestion.

Cite As

Dirk-Jan Kroon (2024). Classic AdaBoost Classifier (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

Fixed boundary bug

Speed improvement (Replaced loops by 1D indexing and bsxfun operations.)
The function now limits features of the test data to the outer-boundaries of training data.

Changed bug : ndims(datafeatures)to size(datafeatures,2)

Solved division by zero, causing NaN

Changed Screenshot and example figure