Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Do it yourself: The User Classifier The User Classifier (mentioned at the end of Section 3.2) allows Weka users to build their o ...

10.2 EXPLORING THE EXPLORER 389 position, and various simple textural features. The training data file is supplied with the Weka ...

390 CHAPTER 10 | THE EXPLORER (a) (b) Figure 10.13Working on the segmentation data with the User Classifier: (a) the data visual ...

10.2 EXPLORING THE EXPLORER 391 Classifypanel and choose the classifier AdaboostM1from the metasection of the hierarchical menu. ...

method, classes to clusters evaluation, compares how well the chosen clusters match a preassigned class in the data. You select ...

10.3 FILTERING ALGORITHMS 393 method. Both are chosen in the usual way and configured with the object editor. You must also deci ...

often applied to a training dataset and then also applied to the test file. If the filter is supervised—for example, if it uses ...

10.3 FILTERING ALGORITHMS 395 and look at its associated object editor, which defines what the filter does and the parameters it ...

396 CHAPTER 10 | THE EXPLORER Table 10.1 Unsupervised attribute filters. Name Function Add Add a new attribute, whose values are ...

10.3 FILTERING ALGORITHMS 397 or first-3,5,9-lastfor attributes 1,2,3,5,9,10,11,12,....The selection can be inverted, affecting ...

One parameter is the name of the Java class that implements the function (which must be a fully qualified name); another is the ...

10.3 FILTERING ALGORITHMS 399 attributes in a dataset into binary ones, replacing each attribute with kvalues by kbinary attribu ...

in some other (previous or future) instance.TimeSeriesDeltareplaces attribute values in the current instance with the difference ...

10.3 FILTERING ALGORITHMS 401 it into a given number of cross-validation folds and reduce it to just one of them. If a random nu ...

Supervised attribute filters Discretize,highlighted in Figure 10.17, uses the MDL method of supervised dis- cretization (Section ...

10.4 LEARNING ALGORITHMS 403 There is a supervised version of the NominalToBinaryfilter that transforms all multivalued nominal ...

404 CHAPTER 10 | THE EXPLORER Table 10.5 Classifier algorithms in Weka. Name Function Bayes AODE Averaged, one-dependence estima ...

10.4 LEARNING ALGORITHMS 405 attributes using supervised discretization.NaiveBayesUpdateableis an incre- mental version that pro ...

with all probabilities conditioned on the class value. This is because the search algorithm defaults to K2 with the maximum numb ...

10.4 LEARNING ALGORITHMS 407 the confidence threshold for pruning (default 0.25), and the minimum number of instances permissibl ...