000			04806nam a22003857a 4500
999			_c9792 _d9792
005			20190509094309.0
008			140731s2015 nju b 001 0 eng
020			_a9789814590075 (hb)
020			_z9789814590082 (ebook)
040			_aDLC _beng _cDLC _erda
042			_apcc
050	0	0	_aQA76.9.D343 _bR654 2014
082	0	0	_a006.312 _222 _bR.L.D
100	1		_aRokach, Lior.
245	1	0	_aData mining with decision trees : _btheory and applications / _cby Lior Rokach, Oded Maimon
250			_a2nd ed.
260			_a[Hackensack] New Jersey : _bWorld Scientific, _cc2015.
300			_a305 p.: _bill.; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_acomputer bookfair2015
504			_aIncludes bibliographical references and index.
505		0	_aAbout the Authors; Preface for the Second Edition; Preface for the First Edition; Contents; 1. Introduction to Decision Trees; 1.1 Data Science; 1.2 Data Mining; 1.3 The Four-Layer Model; 1.4 Knowledge Discovery in Databases (KDD); 1.5 Taxonomy of Data Mining Methods; 1.6 Supervised Methods; 1.6.1 Overview; 1.7 Classification Trees; 1.8 Characteristics of Classification Trees; 1.8.1 Tree Size; 1.8.2 The Hierarchical Nature of Decision Trees; 1.9 Relation to Rule Induction; 2. Training Decision Trees; 2.1 What is Learning?; 2.2 Preparing the Training Set; 2.3 Training the Decision Tree 3. A Generic Algorithm for Top-Down Induction of Decision Trees3.1 Training Set; 3.2 Definition of the Classification Problem; 3.3 Induction Algorithms; 3.4 Probability Estimation in Decision Trees; 3.4.1 Laplace Correction; 3.4.2 No Match; 3.5 Algorithmic Framework for Decision Trees; 3.6 Stopping Criteria; 4. Evaluation of Classification Trees; 4.1 Overview; 4.2 Generalization Error; 4.2.1 Theoretical Estimation of Generalization Error; 4.2.2 Empirical Estimation of Generalization Error; 4.2.3 Alternatives to the Accuracy Measure; 4.2.4 The F-Measure; 4.2.5 Confusion Matrix 4.2.6 Classifier Evaluation under Limited Resources4.2.6.1 ROC Curves; 4.2.6.2 Hit-Rate Curve; 4.2.6.3 Qrecall (Quota Recall); 4.2.6.4 Lift Curve; 4.2.6.5 Pearson Correlation Coefficient; 4.2.6.6 Area Under Curve (AUC); 4.2.6.7 Average Hit-Rate; 4.2.6.8 Average Qrecall; 4.2.6.9 Potential Extract Measure (PEM); 4.2.7 Which Decision Tree Classifier is Better?; 4.2.7.1 McNemar's Test; 4.2.7.2 A Test for the Difference of Two Proportions; 4.2.7.3 The Resampled Paired t Test; 4.2.7.4 The k-fold Cross-validated Paired t Test; 4.3 Computational Complexity; 4.4 Comprehensibility 4.5 Scalability to Large Datasets4.6 Robustness; 4.7 Stability; 4.8 Interestingness Measures; 4.9 Overfitting and Underfitting; 4.10 "No Free Lunch" Theorem; 5. Splitting Criteria; 5.1 Univariate Splitting Criteria; 5.1.1 Overview; 5.1.2 Impurity-based Criteria; 5.1.3 Information Gain; 5.1.4 Gini Index; 5.1.5 Likelihood Ratio Chi-squared Statistics; 5.1.6 DKM Criterion; 5.1.7 Normalized Impurity-based Criteria; 5.1.8 Gain Ratio; 5.1.9 Distance Measure; 5.1.10 Binary Criteria; 5.1.11 Twoing Criterion; 5.1.12 Orthogonal Criterion; 5.1.13 Kolmogorov-Smirnov Criterion 5.1.14 AUC Splitting Criteria5.1.15 Other Univariate Splitting Criteria; 5.1.16 Comparison of Univariate Splitting Criteria; 5.2 Handling Missing Values; 6. Pruning Trees; 6.1 Stopping Criteria; 6.2 Heuristic Pruning; 6.2.1 Overview; 6.2.2 Cost Complexity Pruning; 6.2.3 Reduced Error Pruning; 6.2.4 Minimum Error Pruning (MEP); 6.2.5 Pessimistic Pruning; 6.2.6 Error-BasedPruning (EBP); 6.2.7 Minimum Description Length (MDL) Pruning; 6.2.8 Other Pruning Methods; 6.2.9 Comparison of Pruning Methods; 6.3 Optimal Pruning; 7. Popular Decision Trees Induction Algorithms; 7.1 Overview; 7.2 ID3
520			_aDecision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new ed.
650		0	_aData mining.
650		0	_aDecision trees.
650		0	_aMachine learning.
650		0	_aDecision support systems. _912667
700	1		_aMaimon, Oded.
856			_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/3632
906			_a0 _bvip _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK