8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies
-
S. Ramasamy
Abstract
Meta-learning through ensemble approaches is an intriguing subfield of machine learning research. With this method, a more comprehensive learning model is created by combining many machine learning methods, including neural networks and support vector machines. By using an ensemble of models, meta-learning techniques are able to produce more robust results than individual algorithms alone. In addition, ensemble techniques are advantageous because they can easily be expanded to accommodate additional data sources or algorithms. This approach can also embed more knowledge from the data into a more powerful meta-model, which allows the system to generalize better and discover patterns more accurately. In short, metalearning through ensemble approaches is an effective and useful technique for tackling challenging problems in machine learning.
Abstract
Meta-learning through ensemble approaches is an intriguing subfield of machine learning research. With this method, a more comprehensive learning model is created by combining many machine learning methods, including neural networks and support vector machines. By using an ensemble of models, meta-learning techniques are able to produce more robust results than individual algorithms alone. In addition, ensemble techniques are advantageous because they can easily be expanded to accommodate additional data sources or algorithms. This approach can also embed more knowledge from the data into a more powerful meta-model, which allows the system to generalize better and discover patterns more accurately. In short, metalearning through ensemble approaches is an effective and useful technique for tackling challenging problems in machine learning.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
