# 程序代写代做代考 algorithm matlab decision tree DNA python c++ University of Waterloo

University of Waterloo

ECE 657A: Data and Knowledge Modeling and Analysis

Winter 2017

Assignment 2: Classification and Clustering

Due: Sunday March 19, 2017 (by 11:59pm)

Assignment Type: group, the max team members is 3.

Hand in: one report (PDF) per group, via the LEARN dropbox. Also submit the code / scripts

needed to reproduce your work.

Objective: To gain experience on the use of classification and clustering methods. The emphasis is

on analysis and presentation of results not on code implemented or used. You can use libraries

available in MATLAB, python, R or any other programs available to you. Some instructions for

MATLAB are provided, you need to mention explicitly the source of any other references used.

Data sets (available on the UW ‘LEARN’ system)

Dataset D

(DataD.mat)

This data is the splice junctions on DNA sequences.

The given dataset includes 2200 samples with 57 features, in the matrix

‘fea’. It is a binary class problem. The class labels are either +1 or -1,

given in the vector ‘gnd’. Parameter selection and classification tasks are

conducted on this dataset.

Dataset F

(DataF.mat)

This is a handwritten collection including digits 0 to 9.

The given dataset includes 6200 samples with 256 features, given in

the matrix ‘fea’. This dataset is used in clustering tasks. The sample class

labels (‘gnd’) are used for the purpose of performance evaluation.

I. Parameter Selection and Classification (for dataset D)

Classify dataset D using five classifiers: k-NN, Support Vector Machine (with RBF

kernel), Decision Trees and Neural Networks. The objective is to experiment with

parameter selection in training classifiers and to compare the performance of these well-

known classification methods.

1) Preprocess the given data using the Z-score normalization, and split the data into two

halves, the first half being the training set and the second half being the test set.

(Normally you would do this randomly, but for this assignment a deterministic split

will make the rest of the answers easier to grade).

2) For k-NN you need to evaluate the best value k to use. Using 5-fold cross validation

(the crossvalind function can help) on the training set evaluate k-NN on

the values k=[1, 3, 5, 7, …, 31]. Plot a figure that shows the relationship between

the accuracy and the parameter k. Report the best k in terms of classification accuracy.

3) For the RBF kernel SVM, there are two parameters to be decided: the soft margin

penalty term “c” and the kernel width parameter “sigma”. Again use 5-fold cross

validation on the training set to select the parameter “c” from the set [0.1, 0.5, 1, 2, 5,

10, 20, 50] and select the parameter “sigma” from the set [0.01, 0.05, 0.1, 0.5, 1, 2, 5,

10]. Report the best parameters in terms of classification accuracy including plotting

the ROC curves.

4) Train (at least) six classifiers and report the results:

a) Classify the test set using k-NN, SVM, Decision Trees, Random Forests and Neural

Networks. Use the chosen parameters from the parameter selection process in

question 1.3 for k-NN and SVM. For the next two classifiers use the default setups

for Decision Trees, Random Forests and Neural Networks in the Matlab tool.

b) For the sixth classifier, you should explore the parameters of the Random Forests

and/or Neural Network models to devise your own classifier instance that does

better than the other methods. For example, you could consider a deeper neural

network with multiple layers of auto-encoders or you could modify the Random

Forests using different parameter settings for depth, number of trees.

c) Repeat each classification method 20 times by varying the split of training-test set

as in 1.1. Report the average and standard deviation of classification performance

on the test set regarding: accuracy, precision, recall, and F- Measure. Also report

the training time and classification time of all the methods.

5) Comment on the obtained results, what are the benefits and weaknesses of each method

on this dataset. How could this analysis help to make the choice of the right method to

use for a dataset of this type in the future?

II. Clustering Analysis (for dataset F)

The data has already been normalized into the range of [-1, 1], the sample labels are used

for the purpose of performance evaluation.

Apply PCA to reduce the dimension to be 4, and then conduct the following clustering

analysis.

1) Perform hierarchical clustering using agglomerative algorithms:

a) Stop when the number of clusters is 10 (the same as the number of given classes).

Compare the linkage methods of “single”, “complete”, and “ward (minimum

variance algorithm)”. Evaluate the clustering results in terms of Separation-Index,

Rand-Index, and F-measure. Compare the three linkage types and comment.

b) Fix the linkage type as “ward”, study the number of clusters from 2 to 15, increment

by 1, what is the optimal number of clusters suggested by Separation- Index in

this case?

2) Cluster the data using k-means algorithm.

a) Run the algorithm for the number of clusters k from 2 to 15, increment by 1.

Evaluate the clustering results in terms of Separation-Index, Rand-Index, and F-

measure.

b) Plot these evaluation measures with respect to the number of clusters. What is the

optimal number of clusters suggested by these indexes?

3) Cluster the data using fuzzy c-means algorithm, with number of clusters as 10, and

the fuzzy parameter (the exponent for partition matrix) m = 2.

a) Plot the average cluster membership values for the samples of the digit ‘1’ and

‘8’; explore their overlaps with other clusters.

b) We can produce a hard clustering by setting option(1) of fcm to 1.01 or by making

the max membership value of a sample to be one and the rest of its membership

values zero. Do this and evaluate the clustering results. Comment on the obtained

results and how they compare with k-means. (see

https://www.mathworks.com/help/fuzzy/adjust-fuzzy-overlap-in-

fuzzy-c-means-clustering.html for example of using a range of crispness

value in Matlab).

https://www.mathworks.com/help/fuzzy/adjust-fuzzy-overlap-in-fuzzy-c-means-clustering.html

https://www.mathworks.com/help/fuzzy/adjust-fuzzy-overlap-in-fuzzy-c-means-clustering.html

Notes:

1. For classification methods of k-NN, Naive Bayes, SVM, and Decision trees and

Random Forests (bagged decision trees), Matlab has implemented functions as:

knnclassify, svmtrain

/svmclassify,and fitctree /eval, and TreeBagger

2. There is a well-known SVM library named libSVM, which is implemented in C++ and

includes a matlab interface (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)

3. For the clustering methods of k-means, fuzzy c-means, and hierarchical clustering, Matlab

has implementation as kmeans, fcm, and linkage/clusterdata.

4. For Neural Networks you should be able to use the Matlab patternnet function. See

(http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-

neural-network.html) for a tutorial on setting up neural networks visually and

generating the initial code. For an example of a deeper network built out of autoencoders

look at (https://www.mathworks.com/help/nnet/examples/training-a-deep-

neural-network-for-digit-classification.html)

5. Late submissions (up to 3 days) are accepted with penalty of 10% per day.

Default Parameters:

Decision Trees:

• If using Python or R you can find the default values used in matlab at

https://www.mathworks.com/help/stats/fitctree.html

Random Forests:

• If using Python or R you can find the default values used in matlab at

https://www.mathworks.com/help/stats/treebagger.html

Neural Networks:

• If using Python or R you can find the default values used in matlab at

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-

network.html

http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm/)

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

I. Parameter Selection and Classification (for dataset D)

II. Clustering Analysis (for dataset F)