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DATA MINING AND KNOWLEDGE DISCOVERY 355 Figure 9.46 Selecting an optimal decision tree: (a) splits selected by Gini; (b) splits selected by guesswork
356 KNOWLEDGE ENGINEERING AND DATA MINING Figure 9.47 Gain charts of Class A random sample of the population. The results clearly demonstrate the advantages of the tree constructed with the Gini splits. Can we extract rules from a decision tree? The pass from the root node to the bottom leaf reveals a decision rule. For example, a rule associated with the right bottom leaf in Figure 9.46(a) can be represented as follows: if and and then (Predictor 1 ¼ no) (Predictor 4 ¼ no) (Predictor 6 ¼ no) class ¼ Class A Case study 10: Decision trees for data mining I have the results of a community health survey, and I want to understand which people are at a greater risk of having high blood pressure. Will decision trees work for this problem? A typical task for decision trees is to determine conditions that may lead to certain outcomes. This makes decision trees a good choice for profiling people with high blood pressure, and community health surveys can provide us with the necessary data. High blood pressure, also called hypertension, occurs when the body’s smaller blood vessels narrow. This causes the heart to work harder to maintain the pressure, and although the body can tolerate increased blood pressure for months and even years, eventually the heart may fail. Blood pressure can be categorised as optimal, normal or high. Optimal pressure is below 120/80, normal is between 120/80 and 130/85, and a hypertension is diagnosed when blood pressure is over 140/90. Figure 9.48 shows an example of a data set used in a hypertension study.
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MICHAEL NEGNEVITSKY Artificial Inte
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We work with leading authors to dev
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Pearson Education Limited Edinburgh
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Contents Preface Preface to the sec
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CONTENTS ix 7 Evolutionary computat
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Preface ‘The only way not to succ
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PREFACE xiii In Chapter 3, we prese
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Prefacetothesecondedition The main
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Introduction to knowledgebased inte
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INTELLIGENT MACHINES 3 Figure 1.1 T
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THE HISTORY OF ARTIFICIAL INTELLIGE
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SUMMARY 17 Although fuzzy systems a
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SUMMARY 19 Table 1.1 Period A summa
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QUESTIONS FOR REVIEW 21 or fuzzy se
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REFERENCES 23 Kosko, B. (1997). Fuz
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Rule-based expert systems 2 In whic
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RULES AS A KNOWLEDGE REPRESENTATION
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THE MAIN PLAYERS IN THE EXPERT SYST
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STRUCTURE OF A RULE-BASED EXPERT SY
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FUNDAMENTAL CHARACTERISTICS OF AN E
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FORWARD AND BACKWARD CHAINING INFER
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA: A DEMONSTRATION RULE-BASED E
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CONFLICT RESOLUTION 47 Does MEDIA A
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CONFLICT RESOLUTION 49 . Fire the m
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SUMMARY 51 . Dealing with incomplet
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QUESTIONS FOR REVIEW 53 . Expert sy
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Uncertainty management in rule-base
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BASIC PROBABILITY THEORY 57 Table 3
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BASIC PROBABILITY THEORY 59 single
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BAYESIAN REASONING 61 Figure 3.1 Th
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BAYESIAN REASONING 63 places an eno
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FORECAST: BAYESIAN ACCUMULATION OF
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BIAS OF THE BAYESIAN METHOD 73 Doma
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CERTAINTY FACTORS THEORY AND EVIDEN
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FORECAST: AN APPLICATION OF CERTAIN
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SUMMARY 83 team also could assume t
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REFERENCES 85 . Both Bayesian reaso
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Fuzzy expert systems 4 In which we
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FUZZY SETS 89 Range of logical valu
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FUZZY SETS 91 Figure 4.2 Crisp (a)
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FUZZY SETS 93 Figure 4.3 Crisp (a)
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LINGUISTIC VARIABLES AND HEDGES 95
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OPERATIONS OF FUZZY SETS 97 Table 4
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OPERATIONS OF FUZZY SETS 99 Similar
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OPERATIONS OF FUZZY SETS 101 Figure
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FUZZY RULES 103 Example: NOT (tall
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FUZZY RULES 105 Figure 4.9 Monotoni
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FUZZY INFERENCE 107 determined by f
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FUZZY INFERENCE 109 However, the OR
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FUZZY INFERENCE 111 Step 4: Defuzzi
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FUZZY INFERENCE 113 Figure 4.15 The
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BUILDING A FUZZY EXPERT SYSTEM 115
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SUMMARY 125 4.8 Summary In this cha
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BIBLIOGRAPHY 127 9 What is clipping
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BIBLIOGRAPHY 129 Zadeh, L.A. (1975)
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132 FRAME-BASED EXPERT SYSTEMS Figu
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134 FRAME-BASED EXPERT SYSTEMS of a
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136 FRAME-BASED EXPERT SYSTEMS - -
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140 FRAME-BASED EXPERT SYSTEMS and
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142 FRAME-BASED EXPERT SYSTEMS such
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146 FRAME-BASED EXPERT SYSTEMS valu
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150 FRAME-BASED EXPERT SYSTEMS In a
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156 FRAME-BASED EXPERT SYSTEMS illu
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158 FRAME-BASED EXPERT SYSTEMS Area
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160 FRAME-BASED EXPERT SYSTEMS ) On
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162 FRAME-BASED EXPERT SYSTEMS The
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164 FRAME-BASED EXPERT SYSTEMS Bibl
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166 ARTIFICIAL NEURAL NETWORKS What
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168 ARTIFICIAL NEURAL NETWORKS Tabl
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170 ARTIFICIAL NEURAL NETWORKS Figu
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172 ARTIFICIAL NEURAL NETWORKS Step
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174 ARTIFICIAL NEURAL NETWORKS In F
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176 ARTIFICIAL NEURAL NETWORKS Why
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178 ARTIFICIAL NEURAL NETWORKS To p
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180 ARTIFICIAL NEURAL NETWORKS (b)
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182 ARTIFICIAL NEURAL NETWORKS Then
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184 ARTIFICIAL NEURAL NETWORKS The
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192 ARTIFICIAL NEURAL NETWORKS In o
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194 ARTIFICIAL NEURAL NETWORKS wher
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198 ARTIFICIAL NEURAL NETWORKS In t
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200 ARTIFICIAL NEURAL NETWORKS Ther
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204 ARTIFICIAL NEURAL NETWORKS Here
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206 ARTIFICIAL NEURAL NETWORKS The
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214 ARTIFICIAL NEURAL NETWORKS phys
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216 ARTIFICIAL NEURAL NETWORKS 10 D
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Evolutionary computation 7 In which
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SIMULATION OF NATURAL EVOLUTION 221
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GENETIC ALGORITHMS 223 Figure 7.2 A
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GENETIC ALGORITHMS 225 Figure 7.3 T
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GENETIC ALGORITHMS 227 Figure 7.5 T
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GENETIC ALGORITHMS 229 When necessa
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GENETIC ALGORITHMS 231 A surface of
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WHY GENETIC ALGORITHMS WORK 233 m x
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MAINTENANCE SCHEDULING WITH GENETIC
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EVOLUTION STRATEGIES 243 Define a s
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GENETIC PROGRAMMING 245 Which metho
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GENETIC PROGRAMMING 247 Table 7.3 T
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GENETIC PROGRAMMING 249 Figure 7.15
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QUESTIONS FOR REVIEW 255 . Evolutio
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BIBLIOGRAPHY 257 Whitley, L.D. (199
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260 HYBRID INTELLIGENT SYSTEMS repr
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262 HYBRID INTELLIGENT SYSTEMS enti
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264 HYBRID INTELLIGENT SYSTEMS Figu
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266 HYBRID INTELLIGENT SYSTEMS Now
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278 HYBRID INTELLIGENT SYSTEMS sets
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280 HYBRID INTELLIGENT SYSTEMS How
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282 HYBRID INTELLIGENT SYSTEMS In t
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292 HYBRID INTELLIGENT SYSTEMS word
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294 HYBRID INTELLIGENT SYSTEMS Step
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296 HYBRID INTELLIGENT SYSTEMS Step
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298 HYBRID INTELLIGENT SYSTEMS 4 De
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Knowledge engineering and data mini
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INTRODUCTION, OR WHAT IS KNOWLEDGE
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Page 326 and 327: INTRODUCTION, OR WHAT IS KNOWLEDGE
Page 328 and 329: WILL AN EXPERT SYSTEM WORK FOR MY P
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Page 380 and 381: SUMMARY 361 9.8 Summary In this cha
Page 382 and 383: REFERENCES 363 7 Why are fuzzy syst
Page 384 and 385: Glossary The glossary entries are c
Page 386 and 387: GLOSSARY 367 Back-propagation see B
Page 388 and 389: GLOSSARY 369 Clipping A common meth
Page 390 and 391: GLOSSARY 371 amounts of data in ord
Page 392 and 393: GLOSSARY 373 Evolution strategy A n
Page 394 and 395: GLOSSARY 375 Fuzzy rule A condition
Page 396 and 397: GLOSSARY 377 Heuristic search A sea
Page 398 and 399: GLOSSARY 379 Knowledge base A basic
Page 400 and 401: GLOSSARY 381 Method A procedure ass
Page 402 and 403: GLOSSARY 383 Output layer The last
Page 404 and 405: GLOSSARY 385 Roulette wheel selecti
Page 406 and 407: GLOSSARY 387 the network differs fr
Page 408: GLOSSARY 389 determines the strengt
Page 411 and 412: 392 AI TOOLS AND VENDORS EXSYS, Inc
Page 413 and 414: 394 AI TOOLS AND VENDORS M.4 A powe
Page 415 and 416: 396 AI TOOLS AND VENDORS CynapSys,
Page 417 and 418: 398 AI TOOLS AND VENDORS 215 Parkwa
Page 419 and 420: 400 AI TOOLS AND VENDORS text files
Page 421 and 422: 402 AI TOOLS AND VENDORS TransferTe
Page 423 and 424: 404 AI TOOLS AND VENDORS La Jolla,
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406 AI TOOLS AND VENDORS Fax: +1 (3
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408 INDEX belongs-to 137-8 Berkeley
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410 INDEX fuzzification 107 fuzzifi
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412 INDEX Mamdani, E. 20, 106 Mamda
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INDEX 415 unsupervised learning 200
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