Ensemble Machine Learning : Advances in Research and Applications / M. A. Jabbar, Loveleen Gaur, Abdelkrim Haqiq, editors.

Format:

Book

Contributor:

Jabbar, M. A. (Meerja Akhil), editor.

Gaur, Loveleen, editor.

Haqiq, Abdelkrim, editor.

Series:

Computer science, technology and applications.

Computer Science, Technology and Applications Series

Language:

English

Subjects (All):

Ensemble learning (Machine learning).

Physical Description:

1 online resource (287 pages)

Edition:

First edition.

Place of Publication:

New York : Nova Science Publishers, [2024]

Summary:

"Ensemble Machine Learning: Advances in Research and Applications" delves into the dynamic realm of ensemble methods, offering a comprehensive exploration of its evolution, methodologies, and diverse applications. Chapters are gathered from the collective wisdom of researchers, practitioners, and innovators who have pioneered this ever-evolving domain. This book serves as a compendium, bringing together theoretical foundations, cutting-edge advancements, and practical insights, catering to both seasoned experts and those venturing into the intricate world of ensemble learning. Each chapter encapsulates the essence of collaboration among diverse models, unveiling the intricacies of ensemble techniques, their fusion strategies, and their impact across industries. This book serves as a guiding beacon for enthusiasts, researchers, and practitioners navigating the intricate landscape of ensemble machine learning, fostering innovation, and paving the way for future breakthroughs"-- Provided by publisher.

Contents:

Intro

Contents

Preface

Chapter 1

Ensemble Machine Learning in Deep Fake Detection

Abstract

1. Introduction

2. Deep Fake Creation Techniques

2.1. Deep Face Lab

2.2. Face2Face

3. Deep Fake Detection Techniques

3.1. Machine Learning-Based Approaches

3.2. Face Aliveness Detection

3.3. Behavioral Analysis

4. Ensemble Learning

4.1. Types of Ensemble Learning

4.1.1. Bagging

4.1.2. Boosting

4.2. Applications of Ensemble Learning

5. Ensemble Learning in Deep Fake Detection

Conclusion

Future Scope

References

Chapter 2

Ensemble Machine Learning in Security

2. Ensemble Learning in Security

2.1. Types of Ensemble Learning

2.1.1. Bagging

2.1.2. Benefits and Drawbacks of Bagging

2.1.3. Bagging Applications

2.1.4. Boosting

2.2. Applications of Ensemble Learning

3. Ensemble Learning in Security

3.1. Types of Security Attacks

3.2. Snooping

3.3. Traffic Analysis

3.4. Modification

3.5. Masquerading

3.6. Replaying

3.7. Repudiation

3.8. Denial of Service (DOS)

4. ML Techniques in Security Attacks

5. Existing Methods in Security

5.1. Access Control

5.2. Security Awareness and Training

6. Comparison Table of Conventional and Ensemble Learning Techniques

Chapter 3

The Ensemble Method for Unsupervised Learning

1. Introduction to Ensemble Learning for Unsupervised Learning

2. Ensemble Clustering Methods

3. Advantages of Ensembling in Unsupervised Learning

4. Ensemble Feature Selection

5. Meta-clustering

6. Consensus Clustering

7. Ensemble Anomaly Detection

8. Feature Aggregation in Ensemble Clustering

9. Hybrid Unsupervised Ensembles

10. Evaluation Metrics for Unsupervised Ensembles

11. Handling Ensemble Diversity.

12. Ensemble Learning for Dimensionality Reduction

13. Challenges and Considerations

14. Real-world Applications

15. Future Directions and Trends

Chapter 4

Advances in Boosting Techniques for Machine Learning

1. The original AdaBoost Algorithm

1.1. Walkthrough of the AdaBoost Algorithm

2. Variants of AdaBoost Algorithm

2.1. Real AdaBoost Algorithm

2.2. Gentle AdaBoost Algorithm

2.3. Modest AdaBoost Algorithm

2.4. Parameterized AdaBoost Algorithm

2.5. Margin-Pruning Boost Algorithm

2.6. Penalized AdaBoost Algorithm

3. Advances in Boosting Algorithm

3.1. XGBoost

3.1.1. Advantages of the XGBoost Algorithm

3.2. CatBoost

3.2.1. Advantages of the CatBoost Algorithm

3.3. LightGBM

3.3.1. Advantages of the LightGBM Algorithm

Chapter 5

Base Learners and Weak Learners: The Building Blocks of Ensemble Learning

Introduction

Characteristics of Base Learners

Characteristics of Weak Learners

Base Learners Vs Strong Learners

Comparison between Weak Learners Vs Strong Learners

Various Examples of Different Learner Type

Interpretability of Base Learners and Weak Learners

Base Learners

Weak Learners

Importance of Model Interpretability in Real-World Applications

Emerging Trends in Base Learners and Weak Learners in Ensemble Learning

Chapter 6

An Ensemble Learning Based-Detection Model for Chronic Kidney Disease

2. Literature Review

3. Proposed Methodology

3.1. Dataset Acquisition

3.2. Dataset Pre-processing

3.3. Methodology

3.3.1. Convolutional Neural Network (CNN)

3.3.2. Long Short-Term Memory (LSTM)

3.3.3. CNN-LSTM

3.4. Proposed Ensemble Technique

4. Results and Discussion

4.1. Performance Metrics

4.2. Results

Conclusion.

References

Chapter 7

Ensemble Machine Learning-based Approach for Pan Evaporation Prediction using Observatory Data

2. Materials and Methods

2.1. Area of Research, Data Collection and Pre-Processing

2.2. Development of Machine Learning (ML) based Pan Evaporation Estimation

3. Training of the Models

3.1. Multi-layer Perceptron

3.2. Support Vector Machine (SVM)

3.3. Decision Tree (DT) Regressor

3.4. Random Forest (RF)

3.5. AdaBoost

3.6. XGBoost

3.7. Gradient and Light Gradient Boosting

3.8. CatBoost Boosting

3.9. Histogram Gradient Boosting

3.10. Proposed Ensemble ML based Pan Evaporation Estimation Model

3.11. Empirical Methods for Pan Evaporation Estimation

3.11.1. Linacre Method

3.11.2. Christiansen Method

4. Performance Measures

4.1. Mean Absolute Error (MAE)

4.2. Mean Squared Error

4.3. Root Mean Squared Error

4.4. R-Square

5. Result and Discussion

6. Agricultural Implications (Water Resources Management)

Acknowledgments

Chapter 8

Software-Defined Network-based Machine Learning in IoT Applications

2. Related Work

3. Design of Software Defined Network-based Machine Learning

3.1. Centralized Organization

3.2. SDN-ML Controller Challenges

3.3. Communication Protocol: Open-Flow

4. Background of IoT

4.1. Intelligent IoT Applications

4.1.1. Home Automation

4.1.2. Industry 4.0

4.1.3. Smart City

4.2. The Future of IoT

5. SDN based IoT

5.1. Scalability

5.2. Complexity

5.3. Security

Chapter 9

Ensemble Machine Learning for Personalized Diabetic Retinopathy Management

Overview of Machine Learning in Diabetic Retinopathy

Convolutional Neural Networks (CNNs).

Transfer Learning

Ensemble Learning

Explainable AI (XAI)

Longitudinal Data Analysis

Automated Grading Systems

Challenges and Considerations

Introduction to Ensemble Machine Learning

Ensemble Learning Components

Diversity in Models

Aggregation Techniques

Reduction of Overfitting

Improved Stability

Potential Benefits in Medical Diagnostics

Enhanced Diagnostic Accuracy

Risk Stratification and Customization

Adaptability to Heterogeneous Data

Improving Model Robustness

Facilitating Clinical Decision-Making

Ensemble Machine Learning Techniques

Bagging Methods

Boosting Algorithms

Stacking Models

Voting Classifiers

Weighted Averaging

Ensemble of Different Architectures

Temporal Ensembles

Personalization in Diabetic Retinopathy Management

Role of Ensemble Machine Learning in Personalization

Diverse Factors Influencing Diabetic Retinopathy (DR) Progression and Treatment Response

Chapter 10

Remanning Useful Life Predictions using Machine Learning Algorithms in Smart Buildings

3. Materials and Methods

3.1. Data Preprocessing

Chapter 11

Model Building and Data Analytics for Process Conformance using ML

2. Literature Survey

3. Model Building Overview and Tools

4. ML Workbench

4.1. Data Collection and Preprocessing

4.2. Feature Engineering

4.3. Ensemble Learning Algorithms

4.4. Data Splitting

4.5. Model Training

4.6. Model Evaluation

4.7. Ensemble Building

4.8. Deployment

4.9. Monitoring and Maintenance

5. Result and Analysis

5.1. Data Collection and Preparation

5.2. Feature Engineering

5.3. Model Selection.

5.4. Data Splitting

5.5. Model Training

5.6. Model Evaluation

5.7. Interpretability

5.8. Visualization

5.9. Continuous Improvement

5.10. Deployment

5.11. Monitoring and Maintenance

5.12. Documentation and Reporting

Chapter 12

Ensemble Learning-Based Approaches for Disease Detection of Agricultural Products

2. Ensemble Method

2.1. Boosting

2.1.1. Adaboost

2.1.2. Gradient Boosting

2.1.3. XG Boosting

2.1.4. LightGBM

2.2. Bagging

2.2.1. Bootstrapping

2.2.2. Aggregation

2.3. Stacking

2.5. Disadvantages of Ensemble Learning

3. Ensemble Method for Precision Agriculture

3.1.1. Fungal Diseases

3.1.2. Viral Disease

3.1.3. Bacterial Diseases

3.2. Ensemble Method for Leaf Disease Detection

3.2.1. Coffee Disease Detection

3.2.2. Grape Leaf Detection

3.3. Ensemble Method for Fruit Disease Detection

3.4. Vegetable Detection

4. Conclusion and Recommendations

Chapter 13

A Non-Destructive Technique to Grade the Quality of Mango Fruit: A Review

Internal Quality Parameters for Mango

Problem Identification

Results

Chapter 14

Breast Cancer Image Analysis and Classification Framework by Machine Learning Techniques

Literature Review

Proposed Methodology

Data Preprocessing

Ensemble Learning Algorithms

Results and Discussion

Index

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Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Jabbar, M. A. Ensemble Machine Learning: Advances in Research and Applications

ISBN:

9798895300886