关于机器学习:INF-552机器学习

Homework 6 INF 552,

1. Supervised, Semi-Supervised, and Unsupervised Learning
   (a) Download the Breast Cancer Wisconsin (Diagnostic) Data Set from:
   https://archive.ics.uci.edu/m…
   %28Diagnostic%29. Download the data in https://archive.ics.uci.edu/ml/
   machine-learning-databases/breast-cancer-wisconsin/wdbc.data, which
   has IDs, classes (Benign=B, Malignant=M), and 30 attributes. This data has
   two output classes. Use the first 20% of the positive and negative classes in the
   file as the test set and the rest as the training set.
   (b) Monte-Carlo Simulation: Repeat the following procedures for supervised, unsupervised,
   and semi-supervised learning M = 30 times, and use randomly selected
   train and test data (make sure you use 20% of both the positve and negative
   classes as the test set). Then compare the average scores (accuracy, precision,
   recall, F-score, and AUC) that you obtain from each algorithm.
   i. Supervised Learning: Train an L1-penalized SVM to classify the data.
   Use 5 fold cross validation to choose the penalty parameter. Use normalized
   data. Report the average accuracy, precision, recall, F-score, and AUC, for
   both training and test sets over your M runs. Plot the ROC and report the
   confusion matrix for training and testing in one of the runs.
   ii. Semi-Supervised Learning/ Self-training: select 50% of the positive
   class along with 50% of the negative class in the training set as labeled data
   and the rest as unlabelled data. You can select them randomly.
   A. Train an L1-penalized SVM to classify the labeled data Use normalized
   data. Choose the penalty parameter using 5 fold cross validation.
   B. Find the unlabeled data point that is the farthest to the decision boundary
   of the SVM. Let the SVM label it (ignore its true label), and add it to
   the labeled data, and retrain the SVM. Continue this process until all
   unlabeled data are used. Test the final SVM on the test data andthe
   average accuracy, precision, recall, F-score, and AUC, for both training
   and test sets over your M runs. Plot the ROC and report the confusion
   matrix for training and testing in one of the runs.
   iii. Unsupervised Learning: Run k-means algorithm on the whole training
   set. Ignore the labels of the data, and assume k = 2.
   A. Run the k-means algorithm multiple times. Make sure that you initialize
   the algoritm randomly. How do you make sure that the algorithm was
   not trapped in a local minimum?
   B. Compute the centers of the two clusters and find the closest 30 data
   points to each center. Read the true labels of those 30 data points and
   take a majority poll within them. The majority poll becomes the label
   predicted by k-means for the members of each cluster. Then compare the
   labels provided by k-means with the true labels of the training data and
   report the average accuracy, precision, recall, F-score, and AUC over M
   1
   Homework 6 INF 552, Instructor: Mohammad Reza Rajati
   runs, and ROC and the confusion matrix for one of the runs.1
   C. Classify test data based on their proximity to the centers of the clusters.
   Report the average accuracy, precision, recall, F-score, and AUC over M
   runs, and ROC and the confusion matrix for one of the runs for the test
   data.
   iv. Spectral Clustering: Repeat 1(b)iii using spectral clustering, which is clustering
   based on kernels. Research what spectral clustering is. Use RBF kernel
   with gamma=1 or find a gamma that the two clutsres have the same balance
   as the one in original data set (if the positive class has p and the negative class
   has n samples, the two clusters must have p and n members). Do not label
   data based on their proximity to cluster center, because spectral clustering
   may give you non-convex clusters . Instead, use fit predict method.
   v. One can expect that supervised learning on the full data set works better than
   semi-supervised learning with half of the data set labeled.O ne can expects
   that unsupervised learning underperforms in such situations. Compare the
   results you obtained by those methods.
2. Active Learning Using Support Vector Machines
   (a) Download the banknote authentication Data Set from: https://archive.ics.
   uci.edu/ml/datasets/banknote+authentication. Choose 472 data points randomly
   as the test set, and the remaining 900 points as the training set. This is a
   binary classification problem.
   (b) Repeat each of the following two procedures 50 times. You will have 50 errors for
3. SVMs per each procedure.
   i. Train a SVM with a pool of 10 randomly selected data points from the training
   set using linear kernel and L1 penalty. Select the penalty parameter using
   10-fold cross validation.2 Repeat this process by adding 10 other randomly
   selected data points to the pool, until you use all the 900 points. Do NOT
   replace the samples back into the training set at each step. Calculate the
   test error for each SVM. You will have 90 SVMs that were trained using 10,
   20, 30, … , 900 data points and their 90 test errors. You have implemented
   passive learning.
   1Here we are using k-means as a classifier. The closest 30 data points to each center are labeled by
   experts, so as to use k-means for classification. Obviously, this is a na¨?ve approach.
   2How to choose parameter ranges for SVMs? One can use wide ranges for the parameters and a fine
   grid (e.g. 1000 points) for cross validation; however,this method may be computationally expensive. An
   alternative way is to train the SVM with very large and very small parameters on the whole training data
   and find very large and very small parameters for which the training accuracy is not below a threshold (e.g.,
   70%). Then one can select a fixed number of parameters (e.g., 20) between those points for cross validation.
   For the penalty parameter, usually one has to consider increments in log(λ). For example, if one found that
   the accuracy of a support vector machine will not be below 70% for λ = 10?3 and λ = 106
   , one has to choose
   log(λ) ∈ {3, 2, . . . , 4, 5, 6}. For the Gaussian Kernel parameter, one usually chooses linear increments,e.g.
   σ ∈ {.1, .2, . . . , 2}. When both σ and λ are to be chosen using cross-validation, combinations of very small
   and very large λ’s and σ’s that keep the accuracy above a threshold (e.g.70%) can be used to determine the
   ranges for σ and λ. Please note that these are very rough rules of thumb, not general procedures.
   ii. Train a SVM with a pool of 10 randomly selected data points from the training
   set3 using linear kernel and L1 penalty. Select the parameters of the SVM
   with 10-fold cross validation. Choose the 10 closest data points in the training
   set to the hyperplane of the SVM4 and add them to the pool. Do not replace
   the samples back into the training set. Train a new SVM using the pool.
   Repeat this process until all training data is used. You will have 90 SVMs
   that were trained using 10, 20, 30,…, 900 data points and their 90 test errors.
   You have implemented active learning.
   (c) Average the 50 test errors for each of the incrementally trained 90 SVMs in 2(b)i
   and 2(b)ii. By doing so, you are performing a Monte Carlo simulation. Plot
   average test error versus number of training instances for both active and passive
   learners on the same figure and report your conclusions. Here, you are actually
   obtaining a learning curve by Monte-Carlo simulation.
   3
   If all selected data points are from one class, select another set of 10 data points randomly.
   4You may use the result from linear algebra about the distance of a point from a hyperplane.
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