Machine Learning

Homework 3

Due: September 28, 2007 (midnight)

No late homeworks will be accepted.

For this assignment you will use WEKA to learn about the neural network classifier (called MultilayerPerceptron) and perform a statistical comparison of this classifier along with ConjunctiveRule and J48 using several datasets.

  1. Run the NeuralNetwork classifier on the weather.arff dataset. Use the default parameter settings, and use the training set as the test option. Include in your submission the printed results (weights and statistics) from WEKA. In your report draw graphically the neural network topology (input nodes, hidden nodes, output nodes, connections) used by the classifier. Do not show the weights on your drawing.
  2. WEKA's default parameter settings (among others) for NeuralNetwork are -L 0.3 -M 0.2 -N 500 -H a. Explain in your own words what these mean.
  3. Using WEKA's experimenter application, perform the following experiment.
    1. Choose a "New" experiment.
    2. For the Results Destination section, select ARFF file and provide a file name in which to store the experimental results.
    3. For Experiment Type, choose 10-fold cross-validation and classification.
    4. For Iteration Control, choose the default settings: 10 iterations and data sets first.
    5. Select the following four datasets that come with WEKA: contact-lenses, iris, labor, and weather.
    6. Select the following classifiers with default parameter settings: ConjunctiveRule, J48 and MultilayerPerceptron.
    7. Run the experiment.
    8. Analyze the results by loading the ARFF results file, selecting the following configuration, and perform the test.
      • Testing with: Paired T-Tester (corrected)
      • Comparison field: Percent_incorrect (NOTE: "incorrect", not "correct")
      • Significance: 0.05
      • Test base: rules.ConjunctiveRule
      • Show std. deviations: (checked)
  4. Construct a table of classifiers vs. datasets, and in each entry, enter the error and standard deviation of that classifier on that dataset from the above experiment. Also, add an asterisk to the end of the entry for each dataset/classifier pair for which the classifier outperforms ConjunctiveRule at the 0.05 level.
  5. For the above experiment, what is the lowest significance level (to the nearest 100th) at which the MultilayerPerceptron classifier significantly outperforms the J48 classifier on the labor dataset?
  6. Next, we will use WEKA to generate ROC curves for J48 and MultiLayerPerceptron on the labor dataset. First, we need to generate and save ROC curve data.
    1. Using the WEKA Explorer open the labor dataset under the Preprocess tab.
    2. Under the Classify tab, choose the J48 classifier and click Start to perform a 10-fold cross-validation test.
    3. In the Result list window, right-click on the J48 entry and choose Visualize Threshold Curve and class "good". The visualization window will appear.
    4. Verify the X axis to be False Positive Rate, and the Y axis to be True Positive Rate. You should now see the ROC curve.
    5. Click Save and store the results to a file in ARFF format.
    6. Exit the visualization window and repeat the above for the MultilayerPerceptron classifier with default settings.
    Now, we need to load the data into Excel (or some other charting software) to visualize the ROC curves for both classifiers at once. Here's an outline of the process for Excel.
    1. Edit the two ARFF files containing the threshold curve results saved above and remove everything above and including the "@data" line. Note that the False Positive Rate and True Positive Rate values are the sixth and seventh entries, respectively, in each line.
    2. Open Excel and choose Data -> Import External Data -> Import Data. Browse to the first ARFF file and load it as a comma-delimited file. Do the same for the second ARFF file.
    3. Insert a chart of type scatter line plot and put two lines on the plot: one is TP vs. FP for J48, and one is TP vs. FP for MultilayerPerceptron.
    4. This chart will now show the two ROC curves for J48 and MultilayerPerceptron on the labor dataset.
    5. Nicely format your chart with a title, correct axis titles, correct legend titles, and proper ranges on X and Y axes.
  7. Discuss your conclusions about the performance of J48 vs. MultilayerPerceptron on the labor dataset based on the appearance of the ROC curves.
  8. Email to me ( a zip file containing the following:
    1. Text file containing the raw output of the MultilayerPerceptron run on the weather dataset.
    2. Text file containing the raw output of the first experiment above (result from 3h).
    3. Raw threshold curve data for J48 and MultilayerPerceptron on the labor dataset (the two files you saved in step 6e above).
    4. Nicely-formatted report (MSWord, PDF or PostScript) containing:
      • Drawing of network used in question 1.
      • Description of parameters from question 2.
      • Table summarizing results of first experiment.
      • Minimum significance level at which MultilayerPerceptron outperforms J48 on the labor dataset.
      • Nicely-formatted plot of the two ROC curves.
      • Discussion of performance comparison based on the ROC curves.