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Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded Z |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-91179-8
9786611911799 981-277-172-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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
2.1 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 3.2.6.8 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 4.1.3 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 5.2.3 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 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910450810803321 |
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded Z |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| ISBN |
1-281-91179-8
9786611911799 981-277-172-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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
2.1 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 3.2.6.8 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 4.1.3 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 5.2.3 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 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910784996003321 |
|
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded Z |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| ISBN |
1-281-91179-8
9786611911799 981-277-172-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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
2.1 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 3.2.6.8 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 4.1.3 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 5.2.3 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 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910824725103321 |
|
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||