CA Assignment 1
Data Classification
Implementing Perceptron algorithm
Assessment Information
Assignment Number 1 (of 2)
Weighting 15%
Assignment Circulated 22 Feb
Deadline 19 March 17:00
Submission Mode Electronic via Canvas
Purpose of assessment The purpose of this assignment is to
demonstrate: (1) the understanding of the
Perceptron algorithm; (2) the ability to
implement the Perceptron algorithm for binary
classification; (3) the ability to evaluate a
classification algoirthm; (4) the ability to turn
a binary classification algorithm to a
multi-class classification algorithm using the
1-vs-rest approach; (4) the ability to
incorporate regularisation into classification
algorithm.
Learning outcome assessed (1) A critical awareness of current problems
and research issues in data mining. (3) The
ability to consistently apply knowledge
concerning current data mining research issues
in an original manner and produce work which
is at the forefront of current developments in
the sub-discipline of data mining.
1
Objectives
This assignment requires you to implement the Perceptron algorithm using the Python
programming language.
NOTE
No credit will be given for implementing any other types of classification
algorithms or using an existing library for classification instead of
implementing it by yourself. However, you are allowed to use numpy library
for accessing data structures such as numpy.array. But it is not a requirement
of the assignment to use numpy. You must provide a README file
describing how to run your code to re-produce your results.
Assignment description
Download the CA1data.zip file. Inside, you will find two files: train.data and test.data,
corresponding respectively to the train and test data to be used in this assignment. Each
line in the file represents a different train/test instance. The first four values (separated by
commas) are feature values for four features. The last element is the class label (class-1, class-2
or class-3).
Questions/Tasks
1. (20 marks) Explain the Perceptron algorithm for the binary classification case, providing
its pseudo code.
2. (20 marks) Implement a binary perceptron.
3. (20 marks) Use the binary perceptron to train classifiers to discriminate between
• class 1 and class 2,
• class 2 and class 3, and
• class 1 and class 3.
Report the train and test classification accuracies for each of the three classifiers after
training for 20 iterations. Which pair of classes is most difficult to separate?
4. (20 marks) Extend the binary perceptron that you implemented in part 3 above to
perform multi-class classification using the 1-vs-rest approach. Report the train and test
classification accuracies after training for 20 iterations.
5. (20 marks) Add an `2 regularisation term to your multi-class classifier implemented in
part 4. Set the regularisation coefficient to 0.01, 0.1, 1.0, 10.0, 100.0 and compare the train
and test classification accuracies.
Submission Instructions
Submit via Canvas the following three files
1. the source code for all your programs (do not provide ipython/jupyter/colab
notebooks, instead submit standalone code in a single .py file),
2. a README file (plain text) describing how to compile/run your code to produce the
various results required by the assignment, and
3. a PDF file providing the answers to the questions.
It is extremely important that you provide all the files described above and not just the source
code!