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The University of Adelaide, School of Computer Science

Introduction to Statistical Machine Learning

Semester 2, 2020 Assignment 2: Implementation of AdaBoost

Submission

Instructions and submission guidelines:

• You must sign an assessment declaration coversheet to submit with your assignment.

• Submit your assignment via the Canvas MyUni.

Reading

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: https://en.wikipedia.org/wiki/AdaBoost

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):

Coding

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: https://en.wikipedia.org/wiki/Decision_stump

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)

(5%)

• 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: https://au.mathworks.com/help/stats/fitensemble.html) (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

PDF.

Data

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

features.

There are 569 samples in “wdbc_data.csv”. You will use the first 300 samples for training

and use the remaining part for testing.

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