COMP-SCI 7314 Assignment 2: Implementation of AdaBoost solution


Original Work ?


5/5 - (3 votes)


With this assignment, you will see how Adaboost works on a classification task. The
AdaBoost algorithm is described in the class and more informtion on AdaBoost can be found
on the web pages:

Please read “A Short Introduction to Boosting” by Yoav Freund and Robert E. Schapire, which
can be found here:

If you find difficulties to understand this paper, you may read other tutorial/survey papers
on the same webpage. If and only if you want to know more about Boosting methods, you
are encouraged to read the following papers on Boosting (Optional):


You are provided with the training data (xi; yi); i = 1…., belonging to two classes, with binary
labels yi (If yi is NOT {+1, -1}, you need to convert the labels into {+1, -1} first). You should
use these training data to train an Adaboost classifier.

Please implement the AdaBoost algorithm as given on page 3 of the Freund and Schapire
paper. The algorithm requires that you train a weak learner on data sampled from the
training set. While I expect you to design your AdaBoost program in such a way that you can
plug in any weak learner, I would like you to use Decision Stumps for this assignment.

Decision Stumps are simply one-level decision trees. That is, the learner selects an attribute
for the root of the tree and immediately classifies examples based on their values for that
attribute. Refer to:
To simplify the task, I have also provided a Matlab implementation of Decision Stump
(“build_stump.m”). This is for reference only. Please be aware that you may need to
rewrite/modify the decision stump code for your own needs.

There is a combinatorically large number of experiments that you could run and likewise,
number of measures/settings that you can report against (training time, prediction on
testing set, test time, number of boosting, depth of weak learners – your implementation
only has to provide for Stumps but you can compare against Matlab/Python versions with
deeper weak learners for Adaboost.

If you want, you can extend your code to have trees of some greater depth as weak
learners). This assignment is deliberately open-ended and flexible, meaning that you can
follow to some extent what interests you but also tests your ability to think strategically and
work out what might be the most informative, interesting and efficient things that you could
do (and report on).

Please be aware that there is the law of diminishing returns. Loosely put, you do a great job
and you will get 9/10, and you do an amazing job and you will get 10/10. However, for the
10% extra marks you may well have done 400% more work.
Please start early. This might be a tough algorithm to implement and debug. You can choose
either Matlab, Python, or C/C++ to implement AdaBoost. I would personally suggest Matlab
or Python.

Your code should not rely on any 3rd-party toolbox. Only Matlab’s built-in API’s or Python/
C/C++’s standard libraries are allowed. When you submit your code, please report your
algorithm’s training/test error on the given datasets.

You are also required to submit a report (<10 pages in PDF format), which should have the
following sections (report contributes 45% to the mark; code 55%):

• An algorithmic description of the AdaBoost method. (5%)
• Your understanding of AdaBoost (anything that you believe is relevant to this algorithm)
• Some analysis of your implementation. You should include the training/test error curve
against the number of iterations on the provided data sets in this part (see above. This part
is open-ended) (20% for master students and 25% for undergraduate students)
• You should compare performance with an “inbuild” package (such as fitemsemble in
Matlab: (5% for master students
and 10% for undergraduate students)

• You may also train an SVM and compare the results of SVM with AdaBoost. What do you
observe? (10% for master students. This task is optional for undergraduate students)
In summary, you need to submit (1) the code that implements AdaBoost and (2) a report in


You will use Wisconsin Diagnostic Breast Cancer dataset to test your model. All the data
points are stored in the file “wdbc_data.csv”. The explanation of the data field is given in
“wdbc_names.txt”. You need to predict diagnosis of each sample based on the real-valued
There are 569 samples in “wdbc_data.csv”. You will use the first 300 samples for training
and use the remaining part for testing.