LEADER 05501nam 2200685Ia 450 001 9910450810803321 005 20220209053953.0 010 $a1-281-91179-8 010 $a9786611911799 010 $a981-277-172-7 035 $a(CKB)1000000000407388 035 $a(EBL)1679477 035 $a(OCoLC)886107495 035 $a(SSID)ssj0000293430 035 $a(PQKBManifestationID)11234343 035 $a(PQKBTitleCode)TC0000293430 035 $a(PQKBWorkID)10273347 035 $a(PQKB)10546193 035 $a(MiAaPQ)EBC1679477 035 $a(WSP)00006604 035 $a(Au-PeEL)EBL1679477 035 $a(CaPaEBR)ebr10255816 035 $a(CaONFJC)MIL191179 035 $a(EXLCZ)991000000000407388 100 $a20071223d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining with decision trees$b[electronic resource] /$etheory and applications /$fLior Rokach, Oded Maimon 210 $aSingapore $cWorld Scientific$dc2008 215 $a1 online resource (263 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 69 300 $aDescription based upon print version of record. 311 $a981-277-171-9 320 $aIncludes bibliographical references (p. 215-242) and index. 327 $aPreface; Contents; 1. Introduction to Decision Trees; 1.1 Data Mining and Knowledge Discovery; 1.2 Taxonomy of Data Mining Methods; 1.3 Supervised Methods; 1.3.1 Overview; 1.4 Classification Trees; 1.5 Characteristics of Classification Trees; 1.5.1 Tree Size; 1.5.2 The hierarchical nature of decision trees; 1.6 Relation to Rule Induction; 2. Growing Decision Trees; 2.0.1 Training Set; 2.0.2 Definition of the Classification Problem; 2.0.3 Induction Algorithms; 2.0.4 Probability Estimation in Decision Trees; 2.0.4.1 Laplace Correction; 2.0.4.2 No Match 327 $a2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. Evaluation of Classification Trees; 3.1 Overview; 3.2 Generalization Error; 3.2.1 Theoretical Estimation of Generalization Error; 3.2.2 Empirical Estimation of Generalization Error; 3.2.3 Alternatives to the Accuracy Measure; 3.2.4 The F-Measure; 3.2.5 Confusion Matrix; 3.2.6 Classifier Evaluation under Limited Resources; 3.2.6.1 ROC Curves; 3.2.6.2 Hit Rate Curve; 3.2.6.3 Qrecall (Quota Recall); 3.2.6.4 Lift Curve; 3.2.6.5 Pearson Correlation Coegfficient; 3.2.6.6 Area Under Curve (AUC); 3.2.6.7 Average Hit Rate 327 $a3.2.6.8 Average Qrecall3.2.6.9 Potential Extract Measure (PEM); 3.2.7 Which Decision Tree Classifier is Better?; 3.2.7.1 McNemar's Test; 3.2.7.2 A Test for the Difference of Two Proportions; 3.2.7.3 The Resampled Paired t Test; 3.2.7.4 The k-fold Cross-validated Paired t Test; 3.3 Computational Complexity; 3.4 Comprehensibility; 3.5 Scalability to Large Datasets; 3.6 Robustness; 3.7 Stability; 3.8 Interestingness Measures; 3.9 Overfitting and Underfitting; 3.10 "No Free Lunch" Theorem; 4. Splitting Criteria; 4.1 Univariate Splitting Criteria; 4.1.1 Overview; 4.1.2 Impurity based Criteria 327 $a4.1.3 Information Gain4.1.4 Gini Index; 4.1.5 Likelihood Ratio Chi-squared Statistics; 4.1.6 DKM Criterion; 4.1.7 Normalized Impurity-based Criteria; 4.1.8 Gain Ratio; 4.1.9 Distance Measure; 4.1.10 Binary Criteria; 4.1.11 Twoing Criterion; 4.1.12 Orthogonal Criterion; 4.1.13 Kolmogorov-Smirnov Criterion; 4.1.14 AUC Splitting Criteria; 4.1.15 Other Univariate Splitting Criteria; 4.1.16 Comparison of Univariate Splitting Criteria; 4.2 Handling Missing Values; 5. Pruning Trees; 5.1 Stopping Criteria; 5.2 Heuristic Pruning; 5.2.1 Overview; 5.2.2 Cost Complexity Pruning 327 $a5.2.3 Reduced Error Pruning5.2.4 Minimum Error Pruning (MEP); 5.2.5 Pessimistic Pruning; 5.2.6 Error-Based Pruning (EBP); 5.2.7 Minimum Description Length (MDL) Pruning; 5.2.8 Other Pruning Methods; 5.2.9 Comparison of Pruning Methods; 5.3 Optimal Pruning; 6. Advanced Decision Trees; 6.1 Survey of Common Algorithms for Decision Tree Induction; 6.1.1 ID3; 6.1.2 C4.5; 6.1.3 CART; 6.1.4 CHAID; 6.1.5 QUEST.; 6.1.6 Reference to Other Algorithms; 6.1.7 Advantages and Disadvantages of Decision Trees; 6.1.8 Oblivious Decision Trees; 6.1.9 Decision Trees Inducers for Large Datasets 327 $a6.1.10 Online Adaptive Decision Trees 330 $aThis is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more 410 0$aSeries in machine perception and artificial intelligence ;$vv. 69. 606 $aData mining 606 $aDecision trees 608 $aElectronic books. 615 0$aData mining. 615 0$aDecision trees. 676 $a006.312 700 $aRokach$b Lior$0620362 701 $aMaimon$b Oded Z$0934580 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910450810803321 996 $aData mining with decision trees$92104494 997 $aUNINA