Data Mining: Practical Machine Learning Tools and ... - LIDeCC

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chapter 14Embedded Machine LearningWhen invoking learning schemes from the graphical user interfaces or thecommand line, there is no need to know anything about programming in Java.In this section we show how to access these algorithms from your own code. Indoing so, the advantages of using an object-oriented programming language willbecome clear. From now on, we assume that you have at least some rudimentaryknowledge of Java. In most practical applications of data mining the learningcomponent is an integrated part of a far larger software environment. If theenvironment is written in Java, you can use Weka to solve the learning problemwithout writing any machine learning code yourself.14.1 A simple data mining applicationWe present a simple data mining application for learning a model that classifiestext files into two categories, hit and miss. The application works for arbitrarydocuments: We refer to them as messages. The implementation uses the461
462 CHAPTER 14 | EMBEDDED MACHINE LEARNINGStringToWordVector filter mentioned in Section 10.3 (page 399) to convert themessages into attribute vectors in the manner described in Section 7.3. Weassume that the program is called every time a new file is to be processed. If theWeka user provides a class label for the file, the system uses it for training; ifnot, it classifies it. The decision tree classifier J48 is used to do the work.14.2 Going through the codeFigure 14.1 shows the source code for the application program, implemented ina class called MessageClassifier. The command-line arguments that the main()method accepts are the name of a text file (given by -m), the name of a fileholding an object of class MessageClassifier (-t), and, optionally, the classificationof the message in the file (-c). If the user provides a classification, themessage will be converted into an example for training; if not, the Message-Classifier object will be used to classify it as hit or miss.main()The main() method reads the message into a Java StringBuffer and checkswhether the user has provided a classification for it. Then it reads a Message-Classifier object from the file given by -t and creates a new object of classMessageClassifier if this file does not exist. In either case the resulting object iscalled messageCl. After checking for illegal command-line options, the programcalls the method updateData() to update the training data stored in messageClif a classification has been provided; otherwise, it calls classifyMessage() to classifyit. Finally, the messageCl object is saved back into the file, becauseit may have changed. In the following sections, we first describe how a newMessageClassifier object is created by the constructor MessageClassifier() andthen explain how the two methods updateData() and classifyMessage() work.MessageClassifier()Each time a new MessageClassifier is created, objects for holding the filter andclassifier are generated automatically. The only nontrivial part of the process iscreating a dataset, which is done by the constructor MessageClassifier(). First thedataset’s name is stored as a string. Then an Attribute object is created for eachattribute, one to hold the string corresponding to a text message and the otherfor its class. These objects are stored in a dynamic array of type FastVector.(FastVector is Weka’s own implementation of the standard Java Vector class andis used throughout Weka for historical reasons.)Attributes are created by invoking one of the constructors in the classAttribute. This class has a constructor that takes one parameter—the attribute’sname—and creates a numeric attribute. However, the constructor we use here
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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 317pa
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7.5 COMBINING MULTIPLE MODELS 319mo
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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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chapter 9Introduction to WekaExperi
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9.2 How do you use it?9.2 HOW DO YO
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chapter 10The ExplorerWeka’s main
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10.1 GETTING STARTED 371(a)(b)(c)Fi
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10.1 GETTING STARTED 373deviation.
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10.1 GETTING STARTED 375=== Run inf
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10.1 GETTING STARTED 377Doing it ag
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10.1 GETTING STARTED 379(a)(b)Figur
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10.2 EXPLORING THE EXPLORER 381(a)(
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10.2 EXPLORING THE EXPLORER 383(b)(
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10.2 EXPLORING THE EXPLORER 385(a)(
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10.2 EXPLORING THE EXPLORER 387+ 0.
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10.2 EXPLORING THE EXPLORER 389posi
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10.2 EXPLORING THE EXPLORER 391Figu
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10.3 FILTERING ALGORITHMS 393method
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10.3 FILTERING ALGORITHMS 395(b)Fig
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10.3 FILTERING ALGORITHMS 397or fir
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10.3 FILTERING ALGORITHMS 399attrib
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10.3 FILTERING ALGORITHMS 401it int
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10.4 LEARNING ALGORITHMS 403There i
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10.4 LEARNING ALGORITHMS 405Table 1
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10.4 LEARNING ALGORITHMS 407Figure
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Page 470 and 471: chapter 12The ExperimenterThe Explo
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
Page 478 and 479: 12.5 DISTRIBUTING PROCESSING OVER S
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Page 483 and 484: 450 CHAPTER 13 | THE COMMAND-LINE I
Page 496 and 497: 14.2 GOING THROUGH THE CODE 463/***
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Page 500 and 501: 14.2 GOING THROUGH THE CODE 467}//
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Page 505 and 506: 472 CHAPTER 15 | WRITING NEW LEARNI
Page 518 and 519: ReferencesAdriaans, P., and D. Zant
Page 520 and 521: Bouckaert, R. R. 2004. Bayesian net
Page 522 and 523: REFERENCES 489Cypher, A., editor. 1
Page 524 and 525: REFERENCES 491Fix, E., and J. L. Ho
Page 526 and 527: REFERENCES 493Gennari, J. H., P. La
Page 528 and 529: Conference on Knowledge Discovery a
Page 530 and 531: REFERENCES 497Kushmerick, N., D. S.
Page 532 and 533: Moore, A. W., and M. S. Lee. 1994.
Page 534 and 535: REFERENCES 501editor, Proceedings o
Page 536: REFERENCES 503Webb, G. I., J. Bough
Page 539 and 540: 506 INDEXanomaly detection systems,
Page 541 and 542: 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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