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Machine Learning and Big Data

Concepts, Algorithms, Tools and Applications

Dulhare, Uma N. / Ahmad, Khaleel / Bin Ahmad, Khairol Amali (Herausgeber)

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1. Auflage Oktober 2020
544 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-65474-2
John Wiley & Sons

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Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online survey sites and various news sites, and so on. To date, various use-cases for this application area are being researched and developed. Software applications are already being published and used in various settings from education and training to discover useful hidden patterns and other information like customer choices and market trends that can help organizations make more informed and customer-oriented business decisions.

Combining BD with ML will provide powerful, largely unexplored application areas that will revolutionize practice in Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Fraud Detection, and so on. It is very important to continuously monitor and understand these effects from safety and societal point of view. Hence, the main purpose of this book is for researchers, software developers and practitioners, academicians and students to showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention

Preface xix

Section 1: Theoretical Fundamentals 1

1 Mathematical Foundation 3
Afroz and Basharat Hussain

1.1 Concept of Linear Algebra 3

1.1.1 Introduction 3

1.1.2 Vector Spaces 5

1.1.3 Linear Combination 6

1.1.4 Linearly Dependent and Independent Vectors 7

1.1.5 Linear Span, Basis and Subspace 8

1.1.6 Linear Transformation (or Linear Map) 9

1.1.7 Matrix Representation of Linear Transformation 10

1.1.8 Range and Null Space of Linear Transformation 13

1.1.9 Invertible Linear Transformation 15

1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix 15

1.2.1 Characteristics Polynomial 16

1.2.1.1 Some Results on Eigenvalue 16

1.2.2 Eigendecomposition 18

1.3 Introduction to Calculus 20

1.3.1 Function 20

1.3.2 Limits of Functions 21

1.3.2.1 Some Properties of Limits 22

1.3.2.2 1nfinite Limits 25

1.3.2.3 Limits at Infinity 26

1.3.3 Continuous Functions and Discontinuous Functions 26

1.3.3.1 Discontinuous Functions 27

1.3.3.2 Properties of Continuous Function 27

1.3.4 Differentiation 28

References 29

2 Theory of Probability 31
Parvaze Ahmad Dar and Afroz

2.1 Introduction 31

2.1.1 Definition 31

2.1.1.1 Statistical Definition of Probability 31

2.1.1.2 Mathematical Definition of Probability 32

2.1.2 Some Basic Terms of Probability 32

2.1.2.1 Trial and Event 32

2.1.2.2 Exhaustive Events (Exhaustive Cases) 33

2.1.2.3 Mutually Exclusive Events 33

2.1.2.4 Equally Likely Events 33

2.1.2.5 Certain Event or Sure Event 33

2.1.2.6 Impossible Event or Null Event (Õ) 33

2.1.2.7 Sample Space 34

2.1.2.8 Permutation and Combination 34

2.1.2.9 Examples 35

2.2 Independence in Probability 38

2.2.1 Independent Events 38

2.2.2 Examples: Solve the Following Problems 38

2.3 Conditional Probability 41

2.3.1 Definition 41

2.3.2 Mutually Independent Events 42

2.3.3 Examples 42

2.4 Cumulative Distribution Function 43

2.4.1 Properties 44

2.4.2 Example 44

2.5 Baye's Theorem 46

2.5.1 Theorem 46

2.5.1.1 Examples 47

2.6 Multivariate Gaussian Function 50

2.6.1 Definition 50

2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) 50

2.6.1.2 Degenerate Univariate Gaussian 51

2.6.1.3 Multivariate Gaussian 51

References 51

3 Correlation and Regression 53
Mohd. Abdul Haleem Rizwan

3.1 Introduction 53

3.2 Correlation 54

3.2.1 Positive Correlation and Negative Correlation 54

3.2.2 Simple Correlation and Multiple Correlation 54

3.2.3 Partial Correlation and Total Correlation 54

3.2.4 Correlation Coefficient 55

3.3 Regression 57

3.3.1 Linear Regression 64

3.3.2 Logistic Regression 64

3.3.3 Polynomial Regression 65

3.3.4 Stepwise Regression 66

3.3.5 Ridge Regression 67

3.3.6 Lasso Regression 67

3.3.7 Elastic Net Regression 68

3.4 Conclusion 68

References 69

Section 2: Big Data and Pattern Recognition 71

4 Data Preprocess 73
Md. Sharif Hossen

4.1 Introduction 73

4.1.1 Need of Data Preprocessing 74

4.1.2 Main Tasks in Data Preprocessing 75

4.2 Data Cleaning 77

4.2.1 Missing Data 77

4.2.2 Noisy Data 78

4.3 Data Integration 80

4.3.1 chi2 Correlation Test 82

4.3.2 Correlation Coefficient Test 82

4.3.3 Covariance Test 83

4.4 Data Transformation 83

4.4.1 Normalization 83

4.4.2 Attribute Selection 85

4.4.3 Discretization 86

4.4.4 Concept Hierarchy Generation 86

4.5 Data Reduction 88

4.5.1 Data Cube Aggregation 88

4.5.2 Attribute Subset Selection 90

4.5.3 Numerosity Reduction 91

4.5.4 Dimensionality Reduction 95

4.6 Conclusion 101

Acknowledgements 101

References 101

5 Big Data 105
R. Chinnaiyan

5.1 Introduction 105

5.2 Big Data Evaluation With Its Tools 107

5.3 Architecture of Big Data 107

5.3.1 Big Data Analytics Framework Workflow 107

5.4 Issues and Challenges 109

5.4.1 Volume 109

5.4.2 Variety of Data 110

5.4.3 Velocity 110

5.5 Big Data Analytics Tools 110

5.6 Big Data Use Cases 114

5.6.1 Banking and Finance 114

5.6.2 Fraud Detection 114

5.6.3 Customer Division and Personalized Marketing 114

5.6.4 Customer Support 115

5.6.5 Risk Management 116

5.6.6 Life Time Value Prediction 116

5.6.7 Cyber Security Analytics 117

5.6.8 Insurance Industry 118

5.6.9 Health Care Sector 118

5.6.9.1 Big Data Medical Decision Support 120

5.6.9.2 Big Data-Based Disorder Management 120

5.6.9.3 Big Data-Based Patient Monitoring and Control 120

5.6.9.4 Big Data-Based Human Routine Analytics 120

5.6.10 Internet of Things 121

5.6.11 Weather Forecasting 121

5.7 Where IoT Meets Big Data 122

5.7.1 IoT Platform 122

5.7.2 Sensors or Devices 123

5.7.3 Device Aggregators 123

5.7.4 IoT Gateway 123

5.7.5 Big Data Platform and Tools 124

5.8 Role of Machine Learning For Big Data and IoT 124

5.8.1 Typical Machine Learning Use Cases 125

5.9 Conclusion 126

References 127

6 Pattern Recognition Concepts 131
Ambeshwar Kumar, R. Manikandan and C. Thaventhiran

6.1 Classifier 132

6.1.1 Introduction 132

6.1.2 Explanation-Based Learning 133

6.1.3 Isomorphism and Clique Method 135

6.1.4 Context-Dependent Classification 138

6.1.5 Summary 139

6.2 Feature Processing 140

6.2.1 Introduction 140

6.2.2 Detection and Extracting Edge With Boundary Line 141

6.2.3 Analyzing the Texture 142

6.2.4 Feature Mapping in Consecutive Moving Frame 143

6.2.5 Summary 145

6.3 Clustering 145

6.3.1 Introduction 145

6.3.2 Types of Clustering Algorithms 146

6.3.2.1 Dynamic Clustering Method 148

6.3.2.2 Model-Based Clustering 148

6.3.3 Application 149

6.3.4 Summary 150

6.4 Conclusion 151

References 151

Section 3: Machine Learning: Algorithms & Applications 153

7 Machine Learning 155
Elham Ghanbari and Sara Najafzadeh

7.1 History and Purpose of Machine Learning 155

7.1.1 History of Machine Learning 155

7.1.1.1 What is Machine Learning? 156

7.1.1.2 When the Machine Learning is Needed? 157

7.1.2 Goals and Achievements in Machine Learning 158

7.1.3 Applications of Machine Learning 158

7.1.3.1 Practical Machine Learning Examples 159

7.1.4 Relation to Other Fields 161

7.1.4.1 Data Mining 161

7.1.4.2 Artificial Intelligence 162

7.1.4.3 Computational Statistics 162

7.1.4.4 Probability 163

7.1.5 Limitations of Machine Learning 163

7.2 Concept of Well-Defined Learning Problem 164

7.2.1 Concept Learning 164

7.2.1.1 Concept Representation 166

7.2.1.2 Instance Representation 167

7.2.1.3 The Inductive Learning Hypothesis 167

7.2.2 Concept Learning as Search 167

7.2.2.1 Concept Generality 168

7.3 General-to-Specific Ordering Over Hypotheses 169

7.3.1 Basic Concepts: Hypothesis, Generality 169

7.3.2 Structure of the Hypothesis Space 169

7.3.2.1 Hypothesis Notations 169

7.3.2.2 Hypothesis Evaluations 170

7.3.3 Ordering on Hypotheses: General to Specific 170

7.3.3.1 Most Specific Generalized 171

7.3.3.2 Most General Specialized 173

7.3.3.3 Generalization and Specialization Operators 173

7.3.4 Hypothesis Space Search by Find-S Algorithm 174

7.3.4.1 Properties of the Find-S Algorithm 176

7.3.4.2 Limitations of the Find-S Algorithm 176

7.4 Version Spaces and Candidate Elimination Algorithm 177

7.4.1 Representing Version Spaces 177

7.4.1.1 General Boundary 178

7.4.1.2 Specific Boundary 178

7.4.2 Version Space as Search Strategy 179

7.4.3 The List-Eliminate Method 179

7.4.4 The Candidate-Elimination Method 180

7.4.4.1 Example 181

7.4.4.2 Convergence of Candidate-Elimination Method 183

7.4.4.3 Inductive Bias for Candidate-Elimination 184

7.5 Concepts of Machine Learning Algorithm 185

7.5.1 Types of Learning Algorithms 185

7.5.1.1 Incremental vs. Batch Learning Algorithms 186

7.5.1.2 Offline vs. Online Learning Algorithms 188

7.5.1.3 Inductive vs. Deductive Learning Algorithms 189

7.5.2 A Framework for Machine Learning Algorithms 189

7.5.2.1 Training Data 190

7.5.2.2 Target Function 190

7.5.2.3 Construction Model 191

7.5.2.4 Evaluation 191

7.5.3 Types of Machine Learning Algorithms 194

7.5.3.1 Supervised Learning 196

7.5.3.2 Unsupervised Learning 198

7.5.3.3 Semi Supervised Learning 200

7.5.3.4 Reinforcement Learning 200

7.5.3.5 Deep Learning 202

7.5.4 Types of Machine Learning Problems 203

7.5.4.1 Classification 204

7.5.4.2 Clustering 204

7.5.4.3 Optimization 205

7.5.4.4 Regression 205

Conclusion 205

References 206

8 Performance of Supervised Learning Algorithms on Multi-Variate Datasets 209
Asif Iqbal Hajamydeen and Rabab Alayham Abbas Helmi

8.1 Introduction 209

8.2 Supervised Learning Algorithms 210

8.2.1 Datasets and Experimental Setup 211

8.2.2 Data Treatment/Preprocessing 212

8.3 Classification 212

8.3.1 Support Vector Machines (SVM) 213

8.3.2 Naive Bayes (NB) Algorithm 214

8.3.3 Bayesian Network (BN) 214

8.3.4 Hidden Markov Model (HMM) 215

8.3.5 K-Nearest Neighbour (KNN) 216

8.3.6 Training Time 216

8.4 Neural Network 217

8.4.1 Artificial Neural Networks Architecture 219

8.4.2 Application Areas 222

8.4.3 Artificial Neural Networks and Time Series 224

8.5 Comparisons and Discussions 225

8.5.1 Comparison of Classification Accuracy 225

8.5.2 Forecasting Efficiency Comparison 226

8.5.3 Recurrent Neural Network (RNN) 226

8.5.4 Backpropagation Neural Network (BPNN) 228

8.5.5 General Regression Neural Network 229

8.6 Summary and Conclusion 230

References 231

9 Unsupervised Learning 233
M. Kumara Swamy and Tejaswi Puligilla

9.1 Introduction 233

9.2 Related Work 234

9.3 Unsupervised Learning Algorithms 235

9.4 Classification of Unsupervised Learning Algorithms 238

9.4.1 Hierarchical Methods 238

9.4.2 Partitioning Methods 239

9.4.3 Density-Based Methods 242

9.4.4 Grid-Based Methods 245

9.4.5 Constraint-Based Clustering 245

9.5 Unsupervised Learning Algorithms in ML 246

9.5.1 Parametric Algorithms 246

9.5.2 Non-Parametric Algorithms 246

9.5.3 Dirichlet Process Mixture Model 247

9.5.4 X-Means 248

9.6 Summary and Conclusions 248

References 248

10 Semi-Supervised Learning 251
Manish Devgan, Gaurav Malik and Deepak Kumar Sharma

10.1 Introduction 252

10.1.1 Semi-Supervised Learning 252

10.1.2 Comparison With Other Paradigms 255

10.2 Training Models 257

10.2.1 Self-Training 257

10.2.2 Co-Training 259

10.3 Generative Models--Introduction 261

10.3.1 Image Classification 264

10.3.2 Text Categorization 266

10.3.3 Speech Recognition 268

10.3.4 Baum-Welch Algorithm 268

10.4 S3VMs 270

10.5 Graph-Based Algorithms 274

10.5.1 Mincut 275

10.5.2 Harmonic 276

10.5.3 Manifold Regularization 277

10.6 Multiview Learning 277

10.7 Conclusion 278

References 279

11 Reinforcement Learning 281
Amandeep Singh Bhatia, Mandeep Kaur Saggi, Amit Sundas and Jatinder Ashta

11.1 Introduction: Reinforcement Learning 281

11.1.1 Elements of Reinforcement Learning 283

11.2 Model-Free RL 284

11.2.1 Q-Learning 285

11.2.2 R-Learning 286

11.3 Model-Based RL 287

11.3.1 SARSA Learning 289

11.3.2 Dyna-Q Learning 290

11.3.3 Temporal Difference 291

11.3.3.1 TD(0) Algorithm 292

11.3.3.2 TD(1) Algorithm 293

11.3.3.3 TD(lambda) Algorithm 294

11.3.4 Monte Carlo Method 294

11.3.4.1 Monte Carlo Reinforcement Learning 296

11.3.4.2 Monte Carlo Policy Evaluation 296

11.3.4.3 Monte Carlo Policy Improvement 298

11.4 Conclusion 298

References 299

12 Application of Big Data and Machine Learning 305
Neha Sharma, Sunil Kumar Gautam, Azriel A. Henry and Abhimanyu Kumar

12.1 Introduction 306

12.2 Motivation 307

12.3 Related Work 308

12.4 Application of Big Data and ML 309

12.4.1 Healthcare 309

12.4.2 Banking and Insurance 312

12.4.3 Transportation 314

12.4.4 Media and Entertainment 316

12.4.5 Education 317

12.4.6 Ecosystem Conservation 319

12.4.7 Manufacturing 321

12.4.8 Agriculture 322

12.5 Issues and Challenges 324

12.6 Conclusion 326

References 326

Section 4: Machine Learning's Next Frontier 335

13 Transfer Learning 337
Riyanshi Gupta, Kartik Krishna Bhardwaj and Deepak Kumar Sharma

13.1 Introduction 338

13.1.1 Motivation, Definition, and Representation 338

13.2 Traditional Learning vs. Transfer Learning 338

13.3 Key Takeaways: Functionality 340

13.4 Transfer Learning Methodologies 341

13.5 Inductive Transfer Learning 342

13.6 Unsupervised Transfer Learning 344

13.7 Transductive Transfer Learning 346

13.8 Categories in Transfer Learning 347

13.9 Instance Transfer 348

13.10 Feature Representation Transfer 349

13.11 Parameter Transfer 349

13.12 Relational Knowledge Transfer 350

13.13 Relationship With Deep Learning 351

13.13.1 Transfer Learning in Deep Learning 351

13.13.2 Types of Deep Transfer Learning 352

13.13.3 Adaptation of Domain 352

13.13.4 Domain Confusion 353

13.13.5 Multitask Learning 354

13.13.6 One-Shot Learning 354

13.13.7 Zero-Shot Learning 355

13.14 Applications: Allied Classical Problems 355

13.14.1 Transfer Learning for Natural Language Processing 356

13.14.2 Transfer Learning for Computer Vision 356

13.14.3 Transfer Learning for Audio and Speech 357

13.15 Further Advancements and Conclusion 357

References 358

Section 5: Hands-On and Case Study 361

14 Hands on MAHOUT--Machine Learning Tool
Uma N. Dulhare and Sheikh Gouse

14.1 Introduction to Mahout 363

14.1.1 Features 366

14.1.2 Advantages 366

14.1.3 Disadvantages 366

14.1.4 Application 366

14.2 Installation Steps of Apache Mahout Using Cloudera 367

14.2.1 Installation of VMware Workstation 367

14.2.2 Installation of Cloudera 368

14.2.3 Installation of Mahout 383

14.2.4 Installation of Maven 384

14.2.5 Testing Mahout 386

14.3 Installation Steps of Apache Mahout Using Windows 10 386

14.3.1 Installation of Java 386

14.3.2 Installation of Hadoop 387

14.3.3 Installation of Mahout 387

14.3.4 Installation of Maven 387

14.3.5 Path Setting 388

14.3.6 Hadoop Configuration 391

14.4 Installation Steps of Apache Mahout Using Eclipse 395

14.4.1 Eclipse Installation 395

14.4.2 Installation of Maven Through Eclipse 396

14.4.3 Maven Setup for Mahout Configuration 399

14.4.4 Building the Path- 402

14.4.5 Modifying the pom.xml File 405

14.4.6 Creating the Data File 407

14.4.7 Adding External Jar Files 408

14.4.8 Creating the New Package and Classes 410

14.4.9 Result 411

14.5 Mahout Algorithms 412

14.5.1 Classification 412

14.5.2 Clustering 413

14.5.3 Recommendation 415

14.6 Conclusion 418

References 418

15 Hands-On H2O Machine Learning Tool 423
Uma N. Dulhare, Azmath Mubeen and Khaleel Ahmed

15.1 Introduction 424

15.2 Installation 425

15.2.1 The Process of Installation 425

15.3 Interfaces 431

15.4 Programming Fundamentals 432

15.4.1 Data Manipulation 432

15.4.1.1 Data Types 432

15.4.1.2 Data Import 435

15.4.2 Models 436

15.4.2.1 Model Training 436

15.4.3 Discovering Aspects 437

15.4.3.1 Converting Data Frames 437

15.4.4 H2O Cluster Actions 438

15.4.4.1 H2O Key Value Retrieval 438

15.4.4.2 H2O Cluster Connection 438

15.4.5 Commands 439

15.4.5.1 Cluster Information 439

15.4.5.2 General Data Operations 441

15.4.5.3 String Manipulation Commands 442

15.5 Machine Learning in H2O 442

15.5.1 Supervised Learning 442

15.5.2 Unsupervised Learning 443

15.6 Applications of H2O 443

15.6.1 Deep Learning 443

15.6.2 K-Fold Cross-Authentication or Validation 448

15.6.3 Stacked Ensemble and Random Forest Estimator 450

15.7 Conclusion 452

References 453

16 Case Study: Intrusion Detection System Using Machine Learning 455
Syeda Hajra Mahin, Fahmina Taranum and Reshma Nikhat

16.1 Introduction 456

16.1.1 Components Used to Design the Scenario Include 456

16.1.1.1 Black Hole 456

16.1.1.2 Intrusion Detection System 457

16.1.1.3 Components Used From MATLAB Simulator 458

16.2 System Design 465

16.2.1 Three Sub-Network Architecture 465

16.2.2 Using Classifiers of MATLAB 465

16.3 Existing Proposals 467

16.4 Approaches Used in Designing the Scenario 469

16.4.1 Algorithm Used in QualNet 469

16.4.2 Algorithm Applied in MATLAB 471

16.5 Result Analysis 471

16.5.1 Results From QualNet 471

16.5.1.1 Deployment 471

16.5.1.2 Detection 472

16.5.1.3 Avoidance 473

16.5.1.4 Validation of Conclusion 473

16.5.2 Applying Results to MATLAB 473

16.5.2.1 K-Nearest Neighbor 475

16.5.2.2 SVM 477

16.5.2.3 Decision Tree 477

16.5.2.4 Naive Bayes 479

16.5.2.5 Neural Network 479

16.6 Conclusion 484

References 484

17 Inclusion of Security Features for Implications of Electronic Governance Activities 487
Prabal Pratap and Nripendra Dwivedi

17.1 Introduction 487

17.2 Objective of E-Governance 491

17.3 Role of Identity in E-Governance 493

17.3.1 Identity 493

17.3.2 Identity Management and its Buoyancy Against Identity Theft in E-Governance 494

17.4 Status of E-Governance in Other Countries 496

17.4.1 E-Governance Services in Other Countries Like Australia and South Africa 496

17.4.2 Adaptation of Processes and Methodology for Developing Countries 496

17.4.3 Different Programs Related to E-Governance 499

17.5 Pros and Cons of E-Governance 501

17.6 Challenges of E-Governance in Machine Learning 502

17.7 Conclusion 503

References 503

Index 505
Uma N. Dulhare is a Professor in the Department of Computer Science & Eng., MJCET affiliated to Osmania University, Hyderabad, India. She has more than 20 years teaching experience years with many publications in reputed international conferences, journals and online book chapter contributions. She received her PhD from Osmania University, Hyderabad.

Khaleel Ahmad is an Assistant Professor in the Department of Computer Science & Information Technology at Maulana Azad National Urdu University, Hyderabad, India. He holds a PhD in Computer Science & Engineering. He has published more than 25 papers in refereed journals and conferences as well as edited two books.

Khairol Amali bin Ahmad obtained a BSc in Electrical Engineering in 1992 from the United States Military Academy, West Point, MSc in Military Electronic Systems Engineering in 1999 from Cranfield University, England, and PhD from ISAE-SUPAERO, France in 2015. Currently, he is the Dean of the Engineering Faculty at the National Defense University of Malaysia.