WEKA Waikato Environment for Knowledge Analysis Performing Classification Experiments Prof. Pietro Ducange
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1 WEKA Waikato Environment for Knowledge Analysis Performing Classification Experiments Prof. Pietro Ducange 1
2 The Knowledge Flow Interface It provides an alternative to the Explorer interface The user can select WEKA components from a palette, place them on a layout canvas and connect them together in order to form a knowledge flow for processing and analyzing data. 2
3 Knowledge Flow example (1) Setting up a flow to load an ARFF file and perform a cross-validation using J48 Create a source of data (DataSources tab - ARFFLoader) Connect it to a ARFF file (right click over the ARFFLoader icon - Configure) Specify which attribute is the class (Evaluation tab ClassAssigner) Connect the ArffLoader to the ClassAssigner (right click over the ArffLoader, select the dataset under Connections and link with the ClassAssigner component with a left click Specify which column is the class (right click over the ClassAssigner - choose Configure) Add a CrossValidationFoldMaker component (Evaluation) Connect the ClassAssigner to the CrossValidationFoldMaker (right click over ClassAssigner, select dataset, left click over CrossValidationFoldMaker 3
4 Knowledge Flow example (2) Select the J48 component (classifiers tab) Connect the CrossValidationFoldMaker to J48 TWICE (right click over CrossValidationFoldMaker, first choose trainingset and then testset) Select ClassifierPerformanceEvaluator component (Evaluation tab) Connect J48 to this component (right click over J48, select batchclassifier left click over by ClassifierPerformanceEvaluator Select TextViewer component (Visualization tab) Connect the ClassifierPerformanceEvaluator to the TextViewer (select the text entry from the pop-up menu for ClassifierPerformanceEvaluator) Select GraphViewer component (Vizualization tab) and link to J48 (select the graph entry from the pop-up menu for J48) Start the flow (select start loading from the pop-up menu for the loader) 4
5 Knowledge Flow example (3) 5
6 Knowledge Flow example (4) Select show results from the pop-up menu for the graph viewer Select show results from the pop-up menu for the text viewer 6
7 Knowledge Flow: attribute selection
8 Knowledge Flow: attribute selection Select show results from the pop-up menu for the text viewer connected to the Attribute Selection Block For each fold we can extracted the actual filtered test set!!! 8
9 Knowledge Flow: metaclassification
10 Knowledge Flow: attribute selection Select show results from the pop-up menu for the text viewer connected to the Meta Classifier Block For each fold we can extracted the actual model along with the selected features! 10
11 The Experimenter A robust experimental part involves running several learning schemes on different datasets. The Experimenter interface enables us to set-up large scale experiments. The user can create an experiment that runs several schemes against a series of datasets and then analyze the results to determine if one of the schemes is (statistically) better than the other schemes. 11
12 Simple setup Experiment type: Cross-validation (default), Train/Test Percentage Split (data randomized or order preserved) Number of folds Classification/Regression Iteration control Set the number of repetition and change the order of iterations Datasets Algorithms 12
13 The Analyze panel The number of result lines available Type of results to load: à from the current experiment à from an earlier experiment file à from the database. Type of comparison Significance level How to perform and show the results of the test 13
14 The paired T-test results respect to a control algorithm (C4.5) và the results are statistically better than the control algorithm *à the results are statistically worse than the control algorithm (x/y/z) à counts of the number of times the scheme was better than (x), the same as (y), or worse than (z) the control algorithm 14
15 The paired T-test results respect to a control algorithm (Random Forest) 15
16 Exercise Load the ionosphere dataset and prepare a 5 fold cross validation Perform the classification by using the three different classifiers and identify the most performing one Once selected the best classifier, perform the classification by using a metaclassifier with three different attribute selection methods Which is the best attribute selection method? Which are the most relevant selected attributes? 16
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