COMP3055作業(yè)代寫(xiě)、代做Machine留學(xué)生作業(yè)、代寫(xiě)Python編程作業(yè)、Python作業(yè)代做
COMP3055 Machine Learning Coursework
Deadline: 4pm Friday Dec 21, 2018
Submit an electronic copy via Moodle
The coursework aims to make use of the machine learning techniques learned in this course
to diagnose breast cancer using Wisconsin Diagnostic Breast Cancer (WDBC) dataset.
WDBC contains 569 instances of breast cancer data collected in by professors in the
University of Wisconsin. Each instance is either labeled as M (malignant) or B (benign). In
others words, you are going to solve a binary classification problem. Features are computed
by analyzing a digitized image of a fine needle aspirate (FNA) of a breast mass, instead of
using pixels as raw input. They describe characteristics of the cell nuclei present in the image
(see the following for example images).
In particular, the input include ten real-valued features for each cell nucleus (three in total):
a) Radius (mean of distance from center to points on the perimeter)
b) Texture (standard deviation of gray-scale values)
c) Perimeter
d) Area
e) Smoothness (local variation in radius lengths)
f) Compactness (perimeter2 / area -1.0)
g) Concavity (severity of concave portions of the contour)
h) Concave points (number of concave portions of the contour)
i) Symmetry
j) Fractal dimension (“coastline approximate”-1)
In total, there are 30 features (feature dimension is 30) available for diagnosis. All features
are recorded using four digits for precision.
You will perform the following tasks using Matlab or other languages at your choice (e.g.
Python):
Task 1: You can find WDBC dataset file (wdbc.data) from moodle under coursework
section. The data file is arranged in the way that each line represents an instance of the data.
Within each line, the attribute values are separated by comma (,) and there are total 32
attributes. The first attribute is the patient’s ID. The second attribute is the class label (either
M or B). The rest of the attributes are the input features. Do the following:
1. Load the data from the file into data matrix for the subsequent tasks. In Matlab, you
can use function csvread to do so. Note that you need to read the second attribute
separately as class label and ignore the first attribute. Then you need to read the rest
of attributes as features.
2. Split the data portions: a) select 169 samples as testing data and b) 400 samples for
training.
Task 2: Design and implement a breast cancer diagnosis system using decision tree with
dimension reduction. Do the following
1. Apply PCA to reduce the original input features into new feature vectors with
different dimensions, 3, 5, 7, 9, 11.
2. Use training data to do 10-fold cross validation to train and validate your decision
trees with different input feature vectors (original input and reduced input calculated
in step 1). You can use default parameters for your decision trees according the
library you use.
3. Using test data to compute f1 values for each model and Plot a figure showing result
vs feature dimension.
Task 3: Design and implement a breast cancer diagnosis system using SVM. Do the
following:
1. Use training data to do 10-fold cross validation to train and validate your models. For
the input features, use the one that gives the best performance in task 2. You need to
use linear, polynomial, and rbf kernels for your models. Note that each kernel has
different parameters to set, for example, orders for polynomial model and sigma for
rbf kernels. You can simply use the default parameters for each kernel.
2. Use test data to compute the classification error, precision, recall and f1 for your
models with different kernels in step 1. In the rbf kernel case, draw an ROC curve
with different parameters at your choice.
Task 4 (Optional): Find the best SVM model. You are required do a parameter search for
each kernels and use cross validation to find the best performer. You should also use soft
SVM with different penalty parameters. There are no rule-of-the-thumb on how you should
search the best combination of parameters. Try your best to obtain the highest performance in
terms of precision and recall (f1).
Task 5: Based on your experiences of performing task 2 and task 3 and findings therein, in
your own words, compare and contrast the performances (error rate, precision and recall, f1),
computational complexity (time), level of overfitting of the two approaches. To look at the
level of overfitting, you can compare the performance of a given model on the training data
with test data and see how different they are. State which one you think would be a better
approach to this problem and explain why.
What to submit: A report of no more than 6 pages including all the figures and tables
summarizing how above tasks are done, justification on your decisions involved, and the
results of your analysis. A zipped file with all your source code. Note that you should
properly organize your code with appropriate comments for easy of marking and running.
Marking scheme: this coursework takes 30% of your total marks in this module. The
marking distribution is given in 100 scaling as follows:
1) Completeness of task 1 (10 marks)
2) Completeness of task 2 (30 marks)
3) Completeness of task 3 (30 marks)
4) Completeness of task 5 (10 marks)
5) Report writing (15 marks)http://www.6daixie.com/contents/3/2344.html
6) Coding with proper comments and organization (5 marks)
If you complete task 4, you will get 5 bonus marks in addition to the above marks.

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