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adaboost

version 1.0.0.0 (2.21 MB) by Jaroslaw Tuszynski
Adaboost classification algorithms using 1 or 3 node decision trees

19 Downloads

Updated 16 Nov 2016

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Adaboost package consists of two multi-class adaboost classifiers:
* AdaBoost_samme.m - a class implementing multi-class extension to classic adaboost.M1
algorithm (which was for two-class problems) which was first described in a paper
by Ji Zhu, Saharon Rosset, Hui Zou and Trevor Hastie, “Multi-class
AdaBoost”, January 12, 2006.
https://web.stanford.edu/~hastie/Papers/samme.pdf
* AdaBoost_mult.m - solves same problem using a bank of two-class adaboost
classifiers. A three class problem will use three 2-class classifiers
solving class 1 vs. 2 & 3, class 2 vs. 1 & 3 and class 3 vs. 1 and 2
problems, than each sample is tested with each of the three classifiers
and class is assigned based on the one with the maximum score.
Boosting classifiers work by using a multiple "weak-learner" classifiers.
In this package we provide two weak-learner classifiers:
* decision_stump.m - a class implementing single node decision "tree".
* two_level_decision_tree.m - a class implementing three nodes in two
layers decision "tree" class.

Several helper functions:
* train_stump_2.m - fast low level decision stump function for 2-class problems
* train_stump_N.m - fast low level decision stump function for N-class problems
* save_adaboost_model.m - saves classifier to a CSV file
* load_adaboost_model.m - loads classifier from a CSV file

There are also four demo scripts:
* demo_adaboost_mult_with_decision_stumps.m - demo and testing of AdaBoost_mult classifier with decision_stump weak-learners
* demo_adaboost_mult_with_decision_trees.m - demo and testing of AdaBoost_mult classifier with two_level_decision_tree weak-learners
* demo_adaboost_sammy_with_decision_stump.m - demo and testing of AdaBoost_samme classifier with decision_stump weak-learners
* demo_adaboost_sammy_with_decision_trees.m - demo and testing of AdaBoost_samme classifier with two_level_decision_tree weak-learners

Comments and Ratings (3)

reply to xiaoye wu: total weight of features.

xiaoye wu

Hi, does the section "Check which features are being used" mean the weight of features or the frequency of features being used?

Mina Hamidi

MATLAB Release Compatibility
Created with R2015b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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