Machine Learning and Big Data

Name: Machine Learning and Big Data | Concepts, Algorithms, Tools and Applications
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Concepts, Algorithms, Tools and Applications

Uma N. Dulhare Khaleel Ahmad Khairol Amali Bin Ahmad(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 17. Juli 2020

544 Seiten

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Section 1: Theoretical Fundamentals
Chapter 1 Mathematical Foundation
1.1 Concept of Linear Algebra
1.1.1 Introduction
1.1.2 Vector Spaces
1.1.3 Linear Combination
1.1.4 Linearly Dependent and Independent Vectors
1.1.5 Linear Span, Basis and Subspace
1.1.6 Linear Transformation (or Linear Map)
1.1.7 Matrix Representation of Linear Transformation
1.1.7.1 Transformation Matrix
1.1.8 Range and Null Space of Linear Transformation
1.1.9 Invertible Linear Transformation
1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix
1.2.1 Characteristics Polynomial
1.2.1.1 Some Results on Eigenvalue
1.2.2 Eigendecomposition [11]
1.3 Introduction to Calculus
1.3.1 Function
1.3.2 Limits of Functions
1.3.2.1 Some Properties of Limits
1.3.2.2 1nfinite Limits
1.3.2.3 Limits at Infinity
1.3.3 Continuous Functions and Discontinuous Functions
1.3.3.1 Discontinuous Functions
1.3.3.2 Properties of Continuous Function
1.3.4 Differentiation
References
Chapter 2 Theory of Probability
2.1 Introduction
2.1.1 Definition
2.1.1.1 Statistical Definition of Probability
2.1.1.2 Mathematical Definition of Probability
2.1.2 Some Basic Terms of Probability
2.1.2.1 Trial and Event
2.1.2.2 Exhaustive Events (Exhaustive Cases)
2.1.2.3 Mutually Exclusive Events
2.1.2.4 Equally Likely Events
2.1.2.5 Certain Event or Sure Event
2.1.2.6 Impossible Event or Null Event (.)
2.1.2.7 Sample Space
2.1.2.8 Permutation and Combination
2.1.2.9 Examples
2.2 Independence in Probability
2.2.1 Independent Events
2.2.2 Examples: Solve the Following Problems
2.3 Conditional Probability
2.3.1 Definition
2.3.2 Mutually Independent Events
2.3.3 Examples
2.4 Cumulative Distribution Function
2.4.1 Properties
2.4.2 Example
2.5 Baye's Theorem
2.5.1 Theorem
2.5.1.1 Examples
2.6 Multivariate Gaussian Function
2.6.1 Definition
2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian)
2.6.1.2 Degenerate Univariate Gaussian
2.6.1.3 Multivariate Gaussian
References
Chapter 3 Correlation and Regression
3.1 Introduction
3.2 Correlation
3.2.1 Positive Correlation and Negative Correlation
3.2.2 Simple Correlation and Multiple Correlation
3.2.3 Partial Correlation and Total Correlation
3.2.4 Correlation Coefficient
3.3 Regression
3.3.1 Linear Regression
3.3.2 Logistic Regression
3.3.3 Polynomial Regression
3.3.4 Stepwise Regression
3.3.5 Ridge Regression
3.3.6 Lasso Regression
3.3.7 Elastic Net Regression
3.4 Conclusion
References
Section 2: Big Data and Pattern Recognition
Chapter 4 Data Preprocess
4.1 Introduction
4.1.1 Need of Data Preprocessing
4.1.2 Main Tasks in Data Preprocessing
4.2 Data Cleaning
4.2.1 Missing Data
4.2.2 Noisy Data
4.3 Data Integration
4.3.1 ?2 Correlation Test
4.3.2 Correlation Coefficient Test
4.3.3 Covariance Test
4.4 Data Transformation
4.4.1 Normalization
4.4.2 Attribute Selection
4.4.3 Discretization
4.4.4 Concept Hierarchy Generation
4.5 Data Reduction
4.5.1 Data Cube Aggregation
4.5.2 Attribute Subset Selection
4.5.3 Numerosity Reduction
4.5.4 Dimensionality Reduction
4.6 Conclusion
Acknowledgements
References
Chapter 5 Big Data
5.1 Introduction
5.2 Big Data Evaluation With Its Tools
5.3 Architecture of Big Data
5.3.1 Big Data Analytics Framework Workflow
5.4 Issues and Challenges
5.4.1 Volume
5.4.2 Variety of Data
5.4.3 Velocity
5.5 Big Data Analytics Tools
5.6 Big Data Use Cases
5.6.1 Banking and Finance
5.6.2 Fraud Detection
5.6.3 Customer Division and Personalized Marketing
5.6.4 Customer Support
5.6.5 Risk Management
5.6.6 Life Time Value Prediction
5.6.7 Cyber Security Analytics
5.6.8 Insurance Industry
5.6.9 Health Care Sector
5.6.9.1 Big Data Medical Decision Support
5.6.9.2 Big Data-Based Disorder Management
5.6.9.3 Big Data-Based Patient Monitoring and Control
5.6.9.4 Big Data-Based Human Routine Analytics
5.6.10 Internet of Things
5.6.11 Weather Forecasting
5.7 Where IoT Meets Big Data
5.7.1 IoT Platform
5.7.2 Sensors or Devices
5.7.3 Device Aggregators
5.7.4 IoT Gateway
5.7.5 Big Data Platform and Tools
5.8 Role of Machine Learning For Big Data and IoT
5.8.1 Typical Machine Learning Use Cases
5.9 Conclusion
References
Chapter 6 Pattern Recognition Concepts
6.1 Classifier
6.1.1 Introduction
6.1.2 Explanation-Based Learning
6.1.3 Isomorphism and Clique Method
6.1.4 Context-Dependent Classification
6.1.5 Summary
6.2 Feature Processing
6.2.1 Introduction
6.2.2 Detection and Extracting Edge With Boundary Line
6.2.3 Analyzing the Texture
6.2.4 Feature Mapping in Consecutive Moving Frame
6.2.5 Summary
6.3 Clustering
6.3.1 Introduction
6.3.2 Types of Clustering Algorithms
6.3.2.1 Dynamic Clustering Method
6.3.2.2 Model-Based Clustering
6.3.3 Application
6.3.4 Summary
6.4 Conclusion
References
Section 3: Machine Learning: Algorithms & Applications
Chapter 7 Machine Learning
7.1 History and Purpose of Machine Learning
7.1.1 History of Machine Learning
7.1.1.1 What is Machine Learning?
7.1.1.2 When the Machine Learning is Needed?
7.1.2 Goals and Achievements in Machine Learning
7.1.3 Applications of Machine Learning
7.1.3.1 Practical Machine Learning Examples
7.1.4 Relation to Other Fields
7.1.4.1 Data Mining
7.1.4.2 Artificial Intelligence
7.1.4.3 Computational Statistics
7.1.4.4 Probability
7.1.5 Limitations of Machine Learning
7.2 Concept of Well-Defined Learning Problem
7.2.1 Concept Learning
7.2.1.1 Concept Representation
7.2.1.2 Instance Representation
7.2.1.3 The Inductive Learning Hypothesis
7.2.2 Concept Learning as Search
7.2.2.1 Concept Generality
7.3 General-to-Specific Ordering Over Hypotheses
7.3.1 Basic Concepts: Hypothesis, Generality
7.3.2 Structure of the Hypothesis Space
7.3.2.1 Hypothesis Notations
7.3.2.2 Hypothesis Evaluations
7.3.3 Ordering on Hypotheses: General to Specific
7.3.3.1 Most Specific Generalized
7.3.3.2 Most General Specialized
7.3.3.3 Generalization and Specialization Operators
7.3.4 Hypothesis Space Search by Find-S Algorithm
7.3.4.1 Properties of the Find-S Algorithm
7.3.4.2 Limitations of the Find-S Algorithm
7.4 Version Spaces and Candidate Elimination Algorithm
7.4.1 Representing Version Spaces
7.4.1.1 General Boundary
7.4.1.2 Specific Boundary
7.4.2 Version Space as Search Strategy
7.4.3 The List-Eliminate Method
7.4.4 The Candidate-Elimination Method
7.4.4.1 Example
7.4.4.2 Convergence of Candidate-Elimination Method
7.4.4.3 Inductive Bias for Candidate-Elimination
7.5 Concepts of Machine Learning Algorithm
7.5.1 Types of Learning Algorithms
7.5.1.1 Incremental vs. Batch Learning Algorithms
7.5.1.2 Offline vs. Online Learning Algorithms
7.5.1.3 Inductive vs. Deductive Learning Algorithms
7.5.2 A Framework for Machine Learning Algorithms
7.5.2.1 Training Data
7.5.2.2 Target Function
7.5.2.3 Construction Model
7.5.2.4 Evaluation
7.5.3 Types of Machine Learning Algorithms
7.5.3.1 Supervised Learning
7.5.3.2 Unsupervised Learning
7.5.3.3 Semi-Supervised Learning
7.5.3.4 Reinforcement Learning
7.5.3.5 Deep Learning
7.5.4 Types of Machine Learning Problems
7.5.4.1 Classification
7.5.4.2 Clustering
7.5.4.3 Optimization
7.5.4.4 Regression
Conclusion
References
Chapter 8 Performance of Supervised Learning Algorithms on Multi-Variate Datasets
8.1 Introduction
8.2 Supervised Learning Algorithms
8.2.1 Datasets and Experimental Setup
8.2.2 Data Treatment/Preprocessing
8.3 Classification
8.3.1 Support Vector Machines (SVM)
8.3.2 Naïve Bayes (NB) Algorithm
8.3.3 Bayesian Network (BN)
8.3.4 Hidden Markov Model (HMM)
8.3.5 K-Nearest Neighbour (KNN)
8.3.6 Training Time
8.4 Neural Network
8.4.1 Artificial Neural Networks Architecture
8.4.2 Application Areas
8.4.3 Artificial Neural Networks and Time Series
8.5 Comparisons and Discussions
8.5.1 Comparison of Classification Accuracy
8.5.2 Forecasting Efficiency Comparison
8.5.3 Recurrent Neural Network (RNN)
8.5.4 Backpropagation Neural Network (BPNN)
8.5.5 General Regression Neural Network
8.6 Summary and Conclusion
References
Chapter 9 Unsupervised Learning
9.1 Introduction
9.2 Related Work
9.3 Unsupervised Learning Algorithms
9.4 Classification of Unsupervised Learning Algorithms
9.4.1 Hierarchical Methods
9.4.2 Partitioning Methods
9.4.3 Density-Based Methods
9.4.4 Grid-Based Methods
9.4.5 Constraint-Based Clustering
9.5 Unsupervised Learning Algorithms in ML
9.5.1 Parametric Algorithms
9.5.2 Non-Parametric Algorithms
9.5.3 Dirichlet Process Mixture Model
9.5.4 X-Means
9.6 Summary and Conclusions
References
Chapter 10 Semi-Supervised Learning
10.1 Introduction
10.1.1 Semi-Supervised Learning
10.1.2 Comparison With Other Paradigms
10.2 Training Models
10.2.1 Self-Training
10.2.2 Co-Training
10.3 Generative Models-Introduction
10.3.1 Image Classification
10.3.2 Text Categorization
10.3.3 Speech Recognition
10.3.4 Baum-Welch Algorithm
10.4 S3VMs
10.5 Graph-Based Algorithms
10.5.1 Mincut
10.5.2 Harmonic
10.5.3 Manifold Regularization
10.6 Multiview Learning
10.7 Conclusion
References
Chapter 11 Reinforcement Learning
11.1 Introduction: Reinforcement Learning
11.1.1 Elements of Reinforcement Learning
11.2 Model-Free RL
11.2.1 Q-Learning
11.2.2 R-Learning
11.3 Model-Based RL
11.3.1 SARSA Learning
11.3.2 Dyna-Q Learning
11.3.3 Temporal Difference
11.3.3.1 TD(0) Algorithm
11.3.3.2 TD(1) Algorithm
11.3.3.3 TD(.) Algorithm
11.3.4 Monte Carlo Method
11.3.4.1 Monte Carlo Reinforcement Learning
11.3.4.2 Monte Carlo Policy Evaluation
11.3.4.3 Monte Carlo Policy Improvement
11.4 Conclusion
References
Chapter 12 Application of Big Data and Machine Learning
12.1 Introduction
12.2 Motivation
12.3 Related Work
12.4 Application of Big Data and ML
12.4.1 Healthcare
12.4.2 Banking and Insurance
12.4.3 Transportation
12.4.4 Media and Entertainment
12.4.5 Education
12.4.6 Ecosystem Conservation
12.4.7 Manufacturing
12.4.8 Agriculture
12.5 Issues and Challenges
12.6 Conclusion
References
Section 4: Machine Learning's Next Frontier
Chapter 13 Transfer Learning
13.1 Introduction
13.1.1 Motivation, Definition, and Representation
13.2 Traditional Learning vs. Transfer Learning
13.3 Key Takeaways: Functionality
13.4 Transfer Learning Methodologies
13.5 Inductive Transfer Learning
13.6 Unsupervised Transfer Learning
13.7 Transductive Transfer Learning
13.8 Categories in Transfer Learning
13.9 Instance Transfer
13.10 Feature Representation Transfer
13.11 Parameter Transfer
13.12 Relational Knowledge Transfer
13.13 Relationship With Deep Learning
13.13.1 Transfer Learning in Deep Learning
13.13.2 Types of Deep Transfer Learning
13.13.3 Adaptation of Domain
13.13.4 Domain Confusion
13.13.5 Multitask Learning
13.13.6 One-Shot Learning
13.13.7 Zero-Shot Learning
13.14 Applications: Allied Classical Problems
13.14.1 Transfer Learning for Natural Language Processing
13.14.2 Transfer learning for Computer Vision
13.14.3 Transfer Learning for Audio and Speech
13.15 Further Advancements and Conclusion
References
Section 5: Hands-On and Case Study
Chapter 14 Hands on MAHOUT-Machine Learning Tool
14.1 Introduction to Mahout
14.1.1 Features
14.1.2 Advantages
14.1.3 Disadvantages
14.1.4 Application
14.2 Installation Steps of Apache Mahout Using Cloudera
14.2.1 Installation of VMware Workstation
14.2.2 Installation of Cloudera
14.2.3 Installation of Mahout
14.2.4 Installation of Maven
14.2.5 Testing Mahout
14.3 Installation Steps of Apache Mahout Using Windows 10
14.3.1 Installation of Java
14.3.2 Installation of Hadoop
14.3.3 Installation of Mahout
14.3.4 Installation of Maven
14.3.5 Path Setting
14.3.6 Hadoop Configuration
14.4 Installation Steps of Apache Mahout Using Eclipse
14.4.1 Eclipse Installation
14.4.2 Installation of Maven Through Eclipse
14.4.3 Maven Setup for Mahout Configuration
14.4.4 Building the Path-
14.4.5 Modifying the pom.xml File
14.4.6 Creating the Data File
14.4.7 Adding External Jar Files
14.4.8 Creating the New Package and Classes
14.4.9 Result
14.5 Mahout Algorithms
14.5.1 Classification
14.5.2 Clustering [36, 38, 39]
14.5.3 Recommendation
14.6 Conclusion
References
Chapter 15 Hands-On H2O Machine Learning Tool
15.1 Introduction
15.2 Installation
15.2.1 The Process of Installation
15.3 Interfaces
15.4 Programming Fundamentals
15.4.1 Data Manipulation
15.4.1.1 Data Types
15.4.1.2 Data Import
15.4.2 Models
15.4.2.1 Model Training
15.4.3 Discovering Aspects
15.4.3.1 Converting Data Frames
15.4.4 H2O Cluster Actions
15.4.4.1 H2O Key Value Retrieval
15.4.4.2 H2O Cluster Connection
15.4.5 Commands
15.4.5.1 Cluster Information
15.4.5.2 General Data Operations
15.4.5.3 String Manipulation Commands
15.5 Machine Learning in H2O
15.5.1 Supervised Learning
15.5.2 Unsupervised Learning
15.6 Applications of H2O
15.6.1 Deep Learning
15.6.2 K-Fold Cross-Authentication or Validation
15.6.3 Stacked Ensemble and Random Forest Estimator
15.7 Conclusion
References
Chapter 16 Case Study: Intrusion Detection System Using Machine Learning
16.1 Introduction
16.1.1 Components Used to Design the Scenario Include
16.1.1.1 Black Hole
16.1.1.2 Intrusion Detection System
16.1.1.3 Components Used From MATLAB Simulator
16.2 System Design
16.2.1 Three Sub-Network Architecture
16.2.2 Using Classifiers of MATLAB
16.3 Existing Proposals
16.4 Approaches Used in Designing the Scenario
16.4.1 Algorithm Used in QualNet
16.4.2 Algorithm Applied in MATLAB
16.5 Result Analysis
16.5.1 Results From QualNet
16.5.1.1 Deployment
16.5.1.2 Detection
16.5.1.3 Avoidance
16.5.1.4 Validation of Conclusion
16.5.2 Applying Results to MATLAB
16.5.2.1 K-Nearest Neighbor
16.5.2.2 SVM
16.5.2.3 Decision Tree
16.5.2.4 Naïve Bayes
16.5.2.5 Neural Network
16.6 Conclusion
References
Chapter 17 Inclusion of Security Features for Implications of Electronic Governance Activities
17.1 Introduction
17.2 Objective of E-Governance
17.3 Role of Identity in E-Governance
17.3.1 Identity
17.3.2 Identity Management and its Buoyancy Against Identity Theft in E-Governance
17.4 Status of E-Governance in Other Countries
17.4.1 E-Governance Services in Other Countries Like Australia and South Africa
17.4.2 Adaptation of Processes and Methodology for Developing Countries
17.4.3 Different Programs Related to E-Governance
17.5 Pros and Cons of E-Governance
17.6 Challenges of E-Governance in Machine Learning
17.7 Conclusion
References
Index
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