LNAI 2903 Robustness for Evaluating Rule Generalization Capability in Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma ISBN 9783540206460 354020646X

LNAI 2903 Robustness for Evaluating Rule Generalization Capability in Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma – Ebook PDF Instant Download/Delivery. 9783540206460 ,354020646X
Full download LNAI 2903 Robustness for Evaluating Rule Generalization Capability in Data Mining 1st Edition after payment

Product details:
ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Dianhui Wang, Tharam Dillon, Xiaohang Ma

The evaluation of production rules generated by different data mining algorithms currently depends upon the data set used, thus their generalization capability cannot be estimated. Our method consists of three steps. Firstly, we take a set of rules, copy these rules into a population of rules, and then perturb the parameters of individuals in this population. Secondly, the maximum robustness bounds for the rules is then found using genetic algorithms, where the performance of each individual is measured with respect to the training data. Finally, the relationship between maximum robustness bounds and generalization capability is constructed using statistical analysis for a large number of rules. The significance of this relationship is that it allows the algorithms that mine rules to be compared in terms of robustness bounds, independent of the test data. This technique is applied in a case study to a protein sequence classification problem.

LNAI 2903 Robustness for Evaluating Rule Generalization Capability in Data Mining 1st Edition Table of contents:

Chapter 1: Introduction to Data Mining and Rule-Based Learning

• 1.1. Overview of Data Mining and Knowledge Discovery
• 1.2. Rule-Based Learning Systems: Definitions and Approaches
• 1.3. Types of Rules in Data Mining: Classification, Association, and Regression
• 1.4. Challenges in Rule Learning: Overfitting vs. Generalization
• 1.5. The Role of Robustness in Rule Evaluation and Learning

Chapter 2: Generalization in Data Mining

• 2.1. What is Generalization in Machine Learning?
• 2.2. The Bias-Variance Tradeoff and Its Impact on Generalization
• 2.3. Rule Generalization: Definitions and Key Concepts
• 2.4. Techniques for Measuring Generalization in Rule-Based Models
• 2.5. The Relationship Between Model Complexity and Generalization

Chapter 3: The Concept of Robustness in Rule Evaluation

• 3.1. Defining Robustness in the Context of Data Mining
• 3.2. Types of Robustness: Performance, Stability, and Resilience
• 3.3. Evaluating the Robustness of Rule-Based Systems
• 3.4. Factors Affecting Robustness in Rule Learning Algorithms
• 3.5. Methods for Improving Robustness in Rule-Based Models

Chapter 4: Rule Generalization Metrics and Techniques

• 4.1. Key Metrics for Evaluating Rule Generalization Capability
• 4.2. Cross-Validation and Holdout Methods for Generalization Evaluation
• 4.3. Confidence and Support in Rule-Based Models
• 4.4. Assessing Rule Quality: Precision, Recall, F-Measure
• 4.5. Overfitting and Underfitting in Rule Learning

Chapter 5: Evaluating Rule Generalization with Real-World Data

• 5.1. The Role of Noise and Outliers in Rule Evaluation
• 5.2. Impact of Imbalanced Data on Rule Generalization
• 5.3. Performance Evaluation Across Diverse Data Sets
• 5.4. Case Study 1: Rule Generalization in Customer Behavior Prediction
• 5.5. Case Study 2: Robust Rule Evaluation in Medical Data Mining

Chapter 6: Enhancing Robustness and Generalization in Rule-Based Models

• 6.1. Regularization Techniques for Rule Learning
• 6.2. Pruning Methods to Avoid Overfitting
• 6.3. Ensemble Methods for Improved Rule Generalization
• 6.4. Genetic Algorithms and Evolutionary Strategies for Robust Rule Learning
• 6.5. Hybrid Approaches: Combining Rule Learning with Other Models

Chapter 7: Evaluation Strategies and Performance Comparison

• 7.1. Comparing Rule-Based Systems with Other Machine Learning Approaches
• 7.2. Evaluation Metrics for Model Comparison: Accuracy, Stability, and Efficiency
• 7.3. The Role of Out-of-Sample Validation in Robustness Testing
• 7.4. Case Study 1: Rule Learning vs. Decision Trees in Robustness Evaluation
• 7.5. Case Study 2: Rule Generalization in Large-Scale Data Mining

Chapter 8: Applications of Robust Rule Generalization in Data Mining

• 8.1. Applications in Financial Data Mining and Fraud Detection
• 8.2. Healthcare and Medical Data Mining: Predicting Patient Outcomes
• 8.3. Market Basket Analysis and Consumer Behavior Modeling
• 8.4. Rule-Based Approaches in Text Mining and Natural Language Processing
• 8.5. Robust Rule Evaluation in Environmental and Geospatial Data Mining

Chapter 9: Advanced Topics in Robustness and Rule Generalization

• 9.1. Handling Concept Drift in Rule-Based Learning
• 9.2. Transfer Learning and Generalization Across Domains
• 9.3. Multi-Objective Evaluation of Robustness in Rule Systems
• 9.4. Handling Dynamic and Streaming Data in Rule Evaluation
• 9.5. The Future of Robustness in Rule-Based Learning and Data Mining

Chapter 10: Challenges and Open Research Problems

• 10.1. Scalability Challenges in Evaluating Robust Rule Generalization
• 10.2. The Role of Explainability and Interpretability in Robustness
• 10.3. Evaluating Rule Robustness in Big Data and High-Dimensional Spaces
• 10.4. The Need for Standardized Benchmarking and Evaluation Frameworks
• 10.5. Emerging Trends in Robustness Evaluation and Rule Learning

Chapter 11: Conclusion

• 11.1. Summary of Key Concepts and Insights
• 11.2. The Importance of Robust Rule Generalization in Data Mining
• 11.3. Future Directions for Rule-Based Learning and Robustness
• 11.4. Final Thoughts on the Role of Generalization in Data Science

People also search for LNAI 2903 Robustness for Evaluating Rule Generalization Capability in Data Mining 1st Edition:

 robustness and reproducibility

robustness analysis example

a robust method for estimating optimal treatment regimes

evaluating robustness of neural networks with mixed integer programming