Microsoft Word – COMP3308-assignment2-2018-final.docx
COMP3308 – Introduction to Artificial Intelligence Semester 1, 2018
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Assignment 2: Classification
Deadlines
Submission: 5pm, Friday 18th May, 2018 (week 10)
This assignment is worth 20% of your final mark.
Task description
In this assignment you will implement the K‐Nearest Neighbour and Naïve Bayes algorithms and
evaluate them on a real dataset using the stratified cross validation method. You will also evaluate the
performance of other classifiers on the same dataset using Weka. Finally, you will investigate the
effect of feature selection, in particular the Correlation‐based Feature Selection method (CFS) from
Weka.
Late submissions policy
No late submissions are allowed.
Programming languages
Your implementation can be written in Python, Java, C, C++ or MATLAB. The assignment will be tested
on the University machines, so your code must be compatible with the language version installed on
those machines. You are not allowed to use any of the built‐in classification libraries for the purposes
of this assignment.
Submission and pair work
Your assignment can be completed individually or in pairs. See the submission details section for more
information about how to submit.
This assignment will be submitted using the submission system PASTA
(https://comp3308.it.usyd.edu.au/PASTA/). In order to connect to the website, you’ll need to be
connected to the university VPN. You can read this page to find out how to connect to the VPN. PASTA
will allow you to make as many submissions as you wish, and each submission will provide you with
feedback on each of the components of the assignment. Your last submission before the assignment
deadline will be marked, and the mark displayed on PASTA will be the final mark for your code (12
marks).
1. Data
The dataset for this assignment is the Pima Indian Diabetes dataset. It contains 768 instances
described by 8 numeric attributes. There are two classes ‐ yes and no. Each entry in the dataset
corresponds to a patient’s record; the attributes are personal characteristics and test measurements;
the class shows if the person shows signs of diabetes or not. The patients are from Pima Indian
heritage, hence the name of the dataset.
A copy of the dataset can be downloaded from Canvas. There are 2 files associated with the dataset.
The first file, *.names, describes the data, including the number and the type of the attributes and
classes, as well as their meaning. The second file, *.data, contains the data itself. Your task is to
predict the class, where the class can be yes or no.
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Note: The original dataset can be sourced from UCI Machine Learning Repository. However, you need
to use the dataset available on Canvas as it has been modified for consistency.
2. Data preprocessing
Read the pima‐indians‐diabetes.names file and learn more about the meaning of the attributes
and the classes. Use Weka’s in‐built normalisation filter to normalise the values of each attribute to
make sure they are in the range [0,1]. The normalisation should be done along each column (attribute),
not each row (entry). The class attribute is not normalised – it should remain unchanged. Save the
preprocessed file as pima.csv.
Warning: In order to ensure that Weka can process the data, you will need to add headers to the data
file and save it as a .csv file. You can do this in any text editor. The headers should be removed after
preprocessing.
3. Classification algorithms
K‐Nearest Neighbour
The K‐Nearest Neighbour algorithm should be implemented for any K value and should use Euclidean
distance as the distance measure. If there is ever a tie between the two classes, choose class yes.
Naïve Bayes
The Naïve Bayes should be implemented for numeric attributes, using a probability density function.
Assume a normal distribution, i.e. use the probability density function for a normal distribution. As
before, if there is ever a tie between the two classes, choose class yes.
Note: Carefully read section 6 to find out how your program will be expected to receive input and give
output.
4. 10‐fold stratified cross‐validation
In order to evaluate the performance of the classifiers, you will have to implement 10‐fold stratified
cross‐validation. Your program should be able to show the algorithm’s average accuracy over the 10
folds. This information will be required to complete the report.
Your implementation of 10‐fold stratified cross‐validation will be tested based on your pima‐
folds.csv file. The information about the folds should be stored in pima‐folds.csv in the
following format for each fold:
Name of the fold, fold1 to fold10.
Contents of the fold, with each entry on a new line.
A single blank line to separate the folds from each other.
An example of the pima‐folds.csv file would look as follows (made up data):
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fold1
0.588,0.628,0.574,0.263,0.136,0.463,0.054,0.333,yes
0.243,0.274,0.224,0.894,0.113,0.168,0.735,0.321,no
fold2
0.588,0.628,0.574,0.263,0.136,0.463,0.054,0.333,yes
0.243,0.274,0.224,0.894,0.113,0.168,0.735,0.321,no
…
fold10
0.588,0.628,0.574,0.263,0.136,0.463,0.054,0.333,yes
0.243,0.274,0.224,0.894,0.113,0.168,0.735,0.321,no
Note: The number of instances per fold should not vary by more than one. If the total number of
instances is not divisible by ten, the remaining items should be distributed amongst the folds rather
than being placed in one fold.
5. Feature selection
Correlation‐based feature selection (CFS) is a method for selecting a subset of the original features
(attributes). It searches for the best subset of features, where best is defined by a heuristic which
considers how good the individual features are at predicting the class and how much they correlate
with the other features. Good subsets of features contain features that are highly correlated with the
class and uncorrelated with each other.
Load the pima.csv file in Weka, and apply CFS to reduce the number of features. It is available from
the “Select attributes” tab in Weka. Use “Best‐First Search” as the search method. Save the CSV file
with the reduced number of attributes (this can be done in Weka) and name it pima‐CFS.csv.
Warning: As before, in order to ensure Weka can understand the data, you’ll need to add headers.
Once you are done processing, remove the headers
6. Input and output
Input
Your program will need to be named MyClassifier, however may be written in any of the languages
mentioned in the “Programming languages” section.
Your program should take 3 command line arguments. The first argument is the path to the training
data file, the second is the path to the testing data file, and the third is the name of the algorithm to
be executed (NB for Naïve Bayes and kNN for the Nearest Neighbour, where k is replaced with a
number; e.g. 3NN).
For example, if you were to make a submission in Java, your main class would be
MyClassifier.java, and the following are examples of possible inputs to the program:
$ java MyClassifier pima.csv examples.csv NB
$ java MyClassifier pima‐CFS.csv examples.csv 4NN
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The input testing data file will consist of several new examples to test your data on. The file will not
have headers, will have one example per line, and each line will consist of a normalised value for each
of the non‐class attributes separated by commas. An example input file would look as follows:
0.588,0.628,0.574,0.263,0.136,0.463,0.054,0.333
0.243,0.274,0.224,0.894,0.113,0.168,0.735,0.321
0.738,0.295,0.924,0.113,0.693,0.666,0.486,0.525
The following examples show how the program would be run for each of the submission languages,
assuming we want to run the NB classifier, the training data is in a file called training.txt, and
the testing data is in a file called testing.txt.
Python (version 3.5.3):
python MyClassifier.py training.txt testing.txt NB
Java (version 1.8):
javac MyClassifier.java
java MyClassifier training.txt testing.txt NB
C (gcc version 6.3.0):
gcc –lm ‐w ‐std=c99 –o MyClassifier MyClassifier.c *.c
./MyClassifier training.txt testing.txt NB
C++ (gcc version 6.3.0):
g++ –c MyClassifier.cpp *.cpp *.h
gcc –lstdc++ ‐lm –o MyClassifier *.o
./MyClassifier training.txt testing.txt NB
MATLAB (R2017b):
mcc ‐m ‐o MyClassifier ‐R ‐nodisplay ‐R ‐nojvm MyClassifier
./run_MyClassifier.sh
Note: MATLAB must be run this way (compiled first) to speed up MATLAB running
submissions. The arguments are passed to your MyClassifier function as strings. For
example, the example above will be executed as a function call like this:
MyClassifier(‘training.txt’, ‘testing.txt’, ‘NB’)
Output
Your program will output to standard output (a.k.a. “the console”). The output should be one class
value (yes or no) per line – each line representing your program’s classification of the corresponding
line in the input file. An example output should look as follows:
yes
no
yes
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Note: These outputs are in no way related to the sample inputs given above. If you have any questions
or need any clarifications about program input or output, ask a question on Piazza or ask your tutor.
Since your program will be automatically tested by PASTA, it is important that you follow the
instructions exactly.
7. Weka evaluation
In Weka select 10‐fold cross validation (it is actually 10‐fold stratified cross validation) and run the
following algorithms: ZeroR, 1R, k‐Nearest Neighbor (k‐NN; IBk in Weka), Naïve Bayes (NB), Decision
Tree (DT; J48 in Weka), Multi‐Layer Perceptron (MLP) and Support Vector Machine (SVM; SMO in
Weka).
Compare the performance of the Weka’s classifiers with your k‐Nearest Neighbor and Naïve Bayes
classifiers. Do this for the case without feature selection (using pima.csv) and with CFS feature
selection (using pima‐CFS.csv).
8. Report
You will have to describe your analysis and findings in a report similar to a research paper. Your report
should include 5 sections. There is no minimum or maximum length for the report – you will be marked
on the quality of the content that you provide.
Aim
This section should briefly state the aim of your study and include a paragraph about why this study
is important.
Data
This section should describe the dataset, mentioning the number of attributes and classes. It should
also briefly describe the CFS method and list the attributes selected by the CFS.
Results and discussion
The accuracy results should be presented (in percentage, using 10‐fold cross validation) in the
following table where My1NN, My3NN and MyNB are your implementations of the 1NN, 3NN and NB
algorithms, evaluated using your stratified 10‐fold cross validation.
ZeroR 1R 1NN 3NN NB DT MLP SVM
No feature
selection
CFS
My1NN My3NN MyNB
No feature
selection
CFS
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In the discussion, compare the performance of the classifiers, with and without feature selection.
Compare your implementations of k‐NN and NB with Weka’s. Discuss the effect of the feature
selection – did CFS select a subset of the original features, and if so, did the selected subset make
intuitive sense to you? Was feature selection beneficial, i.e. did it improve accuracy, or have any other
advantages? Why do you think this is the case? Include anything else that you consider important.
Conclusion
Summarise your main findings and, if possible, suggest future work.
Reflection
Write one or two paragraphs describing the most important thing that you have learned throughout
this assignment.
9. Submission Details
This assignment is to be submitted electronically via the PASTA submission system.
Individual submissions setup
The first thing you must do is create an individual group on PASTA. This is due to a limitation of PASTA.
To create a group, follow the instructions below:
1. Click on the “Group Management” button (3 people icon), next to the submit button.
2. Click on the plus button in the bottom right to add a new group.
3. Scroll to the bottom of the list of groups and click on “Join Group” next to the group you just
created.
4. Click on “Lock Group” to lock the group and stop others from joining the group (optional).
Pair submissions setup
The first thing you must do is create/join a group on PASTA. Follow the instructions below:
1. Click on the “Group Management” button (3 people icon), next to the submit button.
2. If your pair has not yet formed a group on PASTA, click on the plus button in the bottom right
to add a new group, otherwise go to step 3.
3. Click on “Join Group” next to your group in the “Other Existing Groups” section.
4. If you wish to stop anyone from joining your group, click on “Lock Group”.
All submissions
Your submission should be zipped together in a single .zip file and include the following:
The report in PDF format.
The source code with a main program called MyClassifier. Valid extensions are .java,
.py, .c, .cpp, .cc, and .m.
Three data files: pima.csv, pima‐CFS.csv and pima‐folds.csv.
A valid submission might look like this:
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submission.zip
|‐ pima.csv
|‐ pima‐folds.csv
|‐ pima‐CSF.csv
|‐ report/
| +‐ report.pdf
|‐ MyClassifier.java
+‐ extrapackage/
|‐ MyClass.java
+‐ OtherClass.java
Upload your submission on PASTA under Assignment 2 ‐ Classification. Make sure you tick the box
saying that you’re submitting on behalf of your group (even if you’re working individually). The
submission won’t work if you don’t.
10. Marking criteria
[12 marks] Code – based on the tests in PASTA; automatic marking
[8 marks] Report:
[0.5 marks] Introduction
What is the aim of the study?
Why is this study (the problem) important?
[0.5 marks] Data – well explained
• Dataset – brief description of the dataset
• Attribute selection – brief summary of CFS and a list of the selected attributes
[4 marks] Results and discussion
• All results presented
• Correct and deep discussion of the results
• Effect of the feature selection – beneficial or not (accuracy, other advantages)
• Comparison between the classifiers (accuracy, other advantages)
[1.5 marks] Conclusions and future work
• Meaningful conclusions based on the results
• Meaningful future work suggested
[0.5 marks] Reflection (meaningful and relevant personal reflection)
[1 marks] English and presentation
• Academic style, grammatical sentences, no spelling mistakes
• Good structure and layout; consistent formatting