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10.3 FILTERING ALGORITHMS 401it into a given number of cross-validation folds and reduce it to just one of them.If a random number seed is provided, the dataset will be shuffled before thesubset is extracted. RemovePercentage removes a given percentage of instances,and RemoveRange removes a certain range of instance numbers. To remove allinstances that have certain values for nominal attributes, or numeric valuesabove or below a certain threshold, use RemoveWithValues. By default allinstances are deleted that exhibit one of a given set of nominal attribute values(if the specified attribute is nominal) or a numeric value below a given threshold(if it is numeric). However, the matching criterion can be inverted.You can remove outliers by applying a classification method to the dataset(specifying it just as the clustering method was specified previously forAddCluster) and use RemoveMisclassified to delete the instances that itmisclassifies.Sparse instancesThe NonSparseToSparse and SparseToNonSparse filters convert between theregular representation of a dataset and its sparse representation (see Section2.4).Supervised filtersSupervised filters are available from the Explorer’s Preprocess panel, just as unsupervisedones are. You need to be careful with them because, despite appearances,they are not really preprocessing operations. We noted this previouslywith regard to discretization—the test data splits must not use the test data’sclass values because these are supposed to be unknown—and it is true for supervisedfilters in general.Because of popular demand, Weka allows you to invoke supervised filters asa preprocessing operation, just like unsupervised filters. However, if you intendto use them for classification you should adopt a different methodology. A metalearneris provided that invokes a filter in a way that wraps the learning algorithminto the filtering mechanism. This filters the test data using the filter thathas been created by the training data. It is also useful for some unsupervisedfilters. For example, in StringToWordVector the dictionary will be created fromthe training data alone: words that are novel in the test data will be discarded.To use a supervised filter in this way, invoke the FilteredClassifier metalearningscheme from in the meta section of the menu displayed by the Classify panel’sChoose button. Figure 10.17(a) shows the object editor for this metalearningscheme. With it you choose a classifier and a filter. Figure 10.17(b) shows themenu of filters.Supervised filters, like unsupervised ones, are divided into attribute andinstance filters, listed in Table 10.3 and Table 10.4.
402 CHAPTER 10 | THE EXPLORER(a)Figure 10.17 Using Weka’s metalearner for discretization: (a) configuring FilteredClassifier,and (b) the menu of filters.(b)Table 10.3NameAttributeSelectionClassOrderDiscretizeNominalToBinarySupervised attribute filters.FunctionProvides access to the same attribute selection methods as theSelect attributes panelRandomize, or otherwise alter, the ordering of class valuesConvert numeric attributes to nominalConvert nominal attributes to binary, using a supervised methodif the class is numericNameTable 10.4Supervised instance filters.FunctionResampleSpreadSubsampleStratifiedRemoveFoldsProduce a random subsample of a dataset, sampling withreplacementProduce a random subsample with a given spread betweenclass frequencies, sampling with replacementOutput a specified stratified cross-validation fold for the datasetSupervised attribute filtersDiscretize, highlighted in Figure 10.17, uses the MDL method of supervised discretization(Section 7.2). You can specify a range of attributes or force the discretizedattribute to be binary. The class must be nominal. By default Fayyadand Irani’s (1993) criterion is used, but Kononenko’s method (1995) is anoption.
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Data MiningPractical Machine Learni
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Publisher:Publishing Services Manag
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viFOREWORDThis book presents this n
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CONTENTSix4 Algorithms: The basic m
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CONTENTSxiGenerating good rules 202
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CONTENTSxiii8 Moving on: Extensions
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CONTENTSxv13 The command-line inter
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xviiiLIST OF FIGURESFigure 4.10 The
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xxLIST OF FIGURESFigure 10.13 Worki
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xxiiLIST OF TABLESTable 5.2 Confide
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xxivPREFACEalchemy. Instead, there
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xxviPREFACEwho interprets them, and
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xxviiiPREFACEin Section 6.3. We hav
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xxxPREFACEration. All who have work
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partIMachine Learning Toolsand Tech
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4 CHAPTER 1 | WHAT’S IT ALL ABOUT
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10 CHAPTER 1 | WHAT’S IT ALL ABOU
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chapter 2Input:Concepts, Instances,
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2.1 WHAT’S A CONCEPT? 43increase
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2.2 WHAT’S IN AN EXAMPLE? 45data
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2.2 WHAT’S IN AN EXAMPLE? 47Table
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2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
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2.3 WHAT’S IN AN ATTRIBUTE? 51Not
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2.4 PREPARING THE INPUT 53cleaned u
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missing values in this dataset). Th
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dividing by the range between the m
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2.4 PREPARING THE INPUT 59a record
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chapter 3Output:Knowledge Represent
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3.2 DECISION TREES 63Alternatively,
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3.3 CLASSIFICATION RULES 65nated by
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3.3 CLASSIFICATION RULES 671abx = 1
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3.4 ASSOCIATION RULES 69yes, then i
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3.5 RULES WITH EXCEPTIONS 71If peta
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3.6 RULES INVOLVING RELATIONS 73ica
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Standard relations include equality
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PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
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3.8 INSTANCE-BASED REPRESENTATION 7
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3.9 Clusters3.9 CLUSTERS 81When clu
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chapter 4Algorithms:The Basic Metho
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4.1 INFERRING RUDIMENTARY RULES 85F
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described overfitting-avoidance bia
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4.2 STATISTICAL MODELING 89Table 4.
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just as we calculated previously. A
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4.2 STATISTICAL MODELING 93Table 4.
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of a document. Instead, a document
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4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
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4.4 COVERING ALGORITHMS: CONSTRUCTI
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4.5 MINING ASSOCIATION RULES 113acc
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4.5 MINING ASSOCIATION RULES 115Tab
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4.5 MINING ASSOCIATION RULES 117whi
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4.6 LINEAR MODELS 119through the da
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4.6 LINEAR MODELS 121However, linea
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4.6 LINEAR MODELS 123n( i)i iÂ 1-x
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4.6 LINEAR MODELS 125Set all weight
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4.6 LINEAR MODELS 127While some ins
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4.7 INSTANCE-BASED LEARNING 129When
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4.7 INSTANCE-BASED LEARNING 131Figu
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4.8 CLUSTERING 137As we saw in Sect
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4.9 FURTHER READING 139can be updat
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4.9 FURTHER READING 141Bayes was an
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chapter 5Credibility:Evaluating Wha
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5.1 TRAINING AND TESTING 145of each
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ather than error rate, so this corr
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5.3 CROSS-VALIDATION 149mediate con
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5.4 OTHER ESTIMATES 151A single 10-
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90% used in 10-fold cross-validatio
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5.5 COMPARING DATA MINING METHODS 1
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5.6 PREDICTING PROBABILITIES 157In
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5.6 PREDICTING PROBABILITIES 159whe
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mental job expected of a loss funct
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y the total number of positives, wh
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5.7 COUNTING THE COST 165different
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5.7 COUNTING THE COST 167Table 5.6D
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5.7 COUNTING THE COST 169100%80%tru
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5.7 COUNTING THE COST 171should cho
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Different terms are used in differe
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5.7 COUNTING THE COST 1750.5Anormal
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5.8 EVALUATING NUMERIC PREDICTION 1
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.10 APPLYING THE MDL PRINCIPLE TO
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5.11 FURTHER READING 185tion theory
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188 CHAPTER 6 | IMPLEMENTATIONS: RE
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chapter 7Transformations:Engineerin
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7.1 ATTRIBUTE SELECTION 287attribut
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7.1 ATTRIBUTE SELECTION 289and less
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tion—and it is much easier to und
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7.1 ATTRIBUTE SELECTION 293outlook
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7.1 ATTRIBUTE SELECTION 295the t-te
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7.2 DISCRETIZING NUMERIC ATTRIBUTES
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7.3 SOME USEFUL TRANSFORMATIONS 305
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7.4 AUTOMATIC DATA CLEANSING 313Int
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7.5 COMBINING MULTIPLE MODELS 315da
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7.5 COMBINING MULTIPLE MODELS 319mo
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7.5 COMBINING MULTIPLE MODELS 321Ra
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7.5 COMBINING MULTIPLE MODELS 323Ho
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7.5 COMBINING MULTIPLE MODELS 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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7.5 COMBINING MULTIPLE MODELS 335Ta
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7.6 Using unlabeled data7.6 USING U
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7.6 USING UNLABELED DATA 339automat
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7.7 FURTHER READING 341deal with we
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7.7 FURTHER READING 343Domingos (19
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346 CHAPTER 8 | MOVING ON: EXTENSIO
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348 CHAPTER 8 | MOVING ON: EXTENSIO
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Page 472 and 473: 12.1 GETTING STARTED 439Dataset,Run
Page 474 and 475: 12.2 SIMPLE SETUP 441cance test of
Page 476 and 477: 12.4 THE ANALYZE PANEL 443advanced
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452 CHAPTER 13 | THE COMMAND-LINE I
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chapter 14Embedded Machine Learning
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14.2 GOING THROUGH THE CODE 463/***
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14.2 GOING THROUGH THE CODE 465// I
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14.2 GOING THROUGH THE CODE 467}//
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14.2 GOING THROUGH THE CODE 469filt
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472 CHAPTER 15 | WRITING NEW LEARNI
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ReferencesAdriaans, P., and D. Zant
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Bouckaert, R. R. 2004. Bayesian net
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REFERENCES 489Cypher, A., editor. 1
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REFERENCES 491Fix, E., and J. L. Ho
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REFERENCES 493Gennari, J. H., P. La
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Conference on Knowledge Discovery a
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REFERENCES 497Kushmerick, N., D. S.
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Moore, A. W., and M. S. Lee. 1994.
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REFERENCES 501editor, Proceedings o
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REFERENCES 503Webb, G. I., J. Bough
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506 INDEXanomaly detection systems,
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508 INDEXcausal relations, 350CfsSu
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510 INDEXCSVLoader, 381cumulative m
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512 INDEXExperimenter, 437-447advan
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514 INDEXimplementation—real-worl
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516 INDEXlistOptions(), 482literary
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518 INDEXnumeric prediction (contin
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520 INDEXrelational data, 49relatio
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522 INDEXsupport vector, 216support
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524 INDEXWinnow, 410Winnow algorith
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