COMP3308-Assignment 2 Classification Solved

1.              Dataset preparation
The 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, pima-indians-diabetes.names, describes the data, including the number and the type of the attributes and classes, as well as their meaning. This file can be opened in any text editor and exists only to give you information about the dataset. Make sure you read the whole file to learn more about the meaning of the attributes and the classes.

The second file, pima-indians-diabetes.data, contains the data itself. Your task is to predict the class, where the class can be yes or no.

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.
Data pre-processing
In order to use the data in this assignment, you’re going to need to do some pre-processing on the pima-indians-diabetes.data file.

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 pre-processed file as pima.csv.

Warning: Weka assumes that any data file you give it has a header row (a row of column names at the top of the file). In order to ensure that Weka can process the data, you will need to add a header row to the data file and save it as a .csv file. It doesn’t matter what you name the columns; just make sure you understand them! You can do this in any text editor. Make sure you remove the headers from pima.csv after pre-processing.
All further activities with the data will be performed using pima.csv as the data file; if you have correctly processed the data, you will no longer need the pima-indians-diabetes.data file.

Include pima.csv in your submission to PASTA, and PASTA will check that you have correctly normalised the file.
2.              Classification algorithms
You will now write two classifiers to predict the class (yes or no) given some new examples. These classifiers will be automatically tested by PASTA, so you need to make sure you follow the input and output instructions carefully.

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.  

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. 5NN).

The file paths given to you (as the first and second arguments) represent files that will be supplied to your program for reading. You can test your submission using any files you like, but PASTA will provide your submission with its own files for testing, so do not assume any specific filenames.

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 newexamples.csv NB 

$ java MyClassifier training.csv testing.csv 4NN 
Training data file 

The input training file will consist of several rows of data, each with 𝑛 attributes plus a single class value (yes or no). The file will not have a header row, 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, followed by a class value. This example has 8 attributes, like pima.csv:

0.084,0.192,0.569,0.274,0.105,0.179,0.090,0.284,yes 

0.091,0.287,0.255,0.234,0.191,0.175,0.174,0.000,no 

0.000,0.929,0.681,0.106,0.238,0.348,0.003,0.000,no 

0.193,0.455,0.379,0.284,0.187,0.355,0.058,0.096,yes 0.489,0.774,0.578,0.218,0.122,0.829,0.104,0.000,no 

0.378,0.839,0.489,0.118,0.173,0.885,0.045,0.691,yes 
Testing data file 

The input testing data file will consist of several new examples to test your data on. The file will not have a header row, 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 could 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 
Note: your program should be able to handle any number of attributes; not just the 8 attributes from pima.csv. You can assume that if the input training file has 𝑛 attributes + a class column, then the testing file will also have 𝑛 attributes.
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 3.7.0): 

python MyClassifier.py training.txt testing.txt NB 
Java (version 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 (R2018a): 

mcc -m -o MyClassifier -R -nodisplay -R -nojvm MyClassifier ./run_MyClassifier.sh <MATLAB_directory training.txt testing.txt NB 
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 could look as follows:

yes no yes 
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.
Include your classifier code in your submission to PASTA, and PASTA will test whether you have correctly implemented the two classifiers.

Make sure that your main code file (MyClassifier.*) is at the top level of the submission folder; i.e. don’t put MyClassifier.* in a subfolder, or PASTA will not be able to find it.
PASTA will only be testing your code for correctness; i.e. does your code get the results that it should, given some expected inputs? Your classifiers are not expected to be able to classify new examples with some specified level of accuracy.

3.              Evaluating your classifiers
Now that you have implemented some classifiers, you need to evaluate them; i.e. find out how well they actually perform as classifiers.

Implementation
In order to evaluate the performance of the classifiers, you will have to implement 10-fold stratified cross-validation as an extension to your classifier code. 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; you need to know the average accuracy of your NB and kNN algorithms (for various values of k).

If you are unsure how to modify your code to show average accuracy using 10-fold cross validation, see Appendix 1 – 10-fold cross validation for a diagram describing how 10-fold cross validation works as an evaluation method.

Make sure your folds are stratified, otherwise your accuracy results can be wrong. For more information see Appendix 2 – Stratification.
Your implementation of 10-fold stratified cross-validation is not required to be part of your PASTA submission, as it will not be automatically tested, however feel free to include it in your submission for reference.

Sample folds
To show that you understand how 10-fold stratified cross-validation works, you will need to generate a file called pima-folds.csv from the original pima.csv. This file can be generated in any manner you choose (manually or using code).

pima-folds.csv should contain 10 folds, each containing the approximately the same number of examples, and the ratio of yes examples to no examples should be approximately the same for each fold.  

Each fold should be in the following format:

•        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):

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.
Include pima-folds.csv in your submission to PASTA, and PASTA will check that you have correctly created the folds and applied stratification.
                

4.              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.

Note: 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
Include pima-CFS.csv in your submission to PASTA, and PASTA will check that you have correctly applied CFS to pima.csv.
5.              Evaluating Weka’s classifiers
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), Support Vector Machine (SVM; SMO in Weka) and Random Forest (RF).

Compare the performance of the Weka’s classifiers with your k-Nearest Neighbour and Naïve Bayes classifiers. Do this for the case without feature selection (using pima.csv) and with CFS feature selection (using pima-CFS.csv).

6.              Report
You will have to describe your analysis and findings in a report similar to a research paper. Your report should include (at least) the following 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. Make sure you put enough detail in each section.

Your report should be written as if you were describing the study to someone who has not seen the data or this assignment before.

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, My5NN and MyNB are your implementations of the 1NN, 5NN and NB algorithms, evaluated using your stratified 10-fold cross validation.

 
ZeroR
1R
1NN
5NN
NB
DT
MLP
SVM
RF
No feature selection
 
 
 
 
 
 
 
 
 
CFS
 
 
 
 
 
 
 
 
 
 

 
My1NN
My5NN
MyNB
No feature selection
 
 
 
CFS
 
 
 
 

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 suggest future work.

Reflection
Write one or two paragraphs describing the most important thing that you have learned throughout this assignment.