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

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14.2 GOING THROUGH THE CODE 463/*** Java program for classifying text messages into two classes.*/import weka.core.Attribute;import weka.core.Instance;import weka.core.Instances;import weka.core.FastVector;import weka.core.Utils;import weka.classifiers.Classifier;import weka.classifiers.trees.J48;import weka.filters.Filter;import weka.filters.unsupervised.attribute.StringToWordVector;import java.io.*;public class MessageClassifier implements Serializable {/* The training data gathered so far. */private Instances m_Data = null;/* The filter used to generate the word counts. */private StringToWordVector m_Filter = new StringToWordVector();/* The actual classifier. */private Classifier m_Classifier = new J48();/* Whether the model is up to date. */private boolean m_UpToDate;/*** Constructs empty training dataset.*/public MessageClassifier() throws Exception {String nameOfDataset = "MessageClassificationProblem";// Create vector of attributes.FastVector attributes = new FastVector(2);// Add attribute for holding messages.attributes.addElement(new Attribute("Message", (FastVector)null));Figure 14.1 Source code for the message classifier.
464 CHAPTER 14 | EMBEDDED MACHINE LEARNING// Add class attribute.FastVector classValues = new FastVector(2);classValues.addElement("miss");classValues.addElement("hit");attributes.addElement(new Attribute("Class", classValues));// Create dataset with initial capacity of 100, and set index of class.m_Data = new Instances(nameOfDataset, attributes, 100);m_Data.setClassIndex(m_Data.numAttributes() - 1);}/*** Updates data using the given training message.*/public void updateData(String message, String classValue) throws Exception {// Make message into instance.Instance instance = makeInstance(message, m_Data);// Set class value for instance.instance.setClassValue(classValue);// Add instance to training data.m_Data.add(instance);m_UpToDate = false;}/*** Classifies a given message.*/public void classifyMessage(String message) throws Exception {// Check whether classifier has been built.if (m_Data.numInstances() == 0) {throw new Exception("No classifier available.");}// Check whether classifier and filter are up to date.if (!m_UpToDate) {Figure 14.1 (continued)
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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 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 331ev
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7.5 COMBINING MULTIPLE MODELS 333be
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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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10.4 LEARNING ALGORITHMS 409value (
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10.4 LEARNING ALGORITHMS 411Neural
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Page 461 and 462: 428 CHAPTER 11 | THE KNOWLEDGE FLOW
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 494 and 495: chapter 14Embedded Machine Learning
Page 498 and 499: 14.2 GOING THROUGH THE CODE 465// I
Page 500 and 501: 14.2 GOING THROUGH THE CODE 467}//
Page 502: 14.2 GOING THROUGH THE CODE 469filt
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
Page 543 and 544: 510 INDEXCSVLoader, 381cumulative m
Page 545 and 546: 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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