Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021 (Smart Innovation, Systems and Technologies, 281)

✍ Scribed by Janmenjoy Nayak (editor), H.S. Behera (editor), Bighnaraj Naik (editor), S. Vimal (editor), Danilo Pelusi (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 757
• Category: Library

✦ Synopsis

This book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11–12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

✦ Table of Contents

ICCIDM Committee
Preface
Acknowledgements
Contents
About the Editors
Multi-Sensor Data Fusion for Occupancy Detection Using Dempster–Shafer Theory
1 Introduction
2 Proposed Method
2.1 Logistic Regression
2.2 K-Nearest Neighbors
2.3 Support Vector Machines
2.4 Decision Tree Classifier
2.5 Random Forest Classifier
2.6 Proposed Methodology
3 Dempster–Shafer Evidence Theory
3.1 Frame of Discernment
3.2 Mass Function
3.3 Degree of Belief and Plausibility Degree
3.4 Dempster’s Rule of Combination
3.5 Temperature and Humidity Evaluation
4 Results and Discussion
5 Conclusion
References
Sentiment Analysis: A Recent Survey with Applications and a Proposed Ensemble Algorithm
1 Introduction
2 Sentiment Analysis and Its Approaches
2.1 Supervised-Based Learning Approach
2.2 Unsupervised-Based Learning Approach
3 Applications of Sentiment Analysis
4 Proposed Methodology
4.1 Preprocessing
4.2 Feature Extraction and Feature Selection
4.3 Feature Training Using Various Machine Learning Classifiers
4.4 Applied Ensemble Techniques and Evaluate the Performance
5 Illustrative Example
6 Conclusion
References
An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic
1 Introduction
2 Related Works
3 Methodology
3.1 Image Preprocessing
3.2 Deep Learning Architecture
3.3 Face Mask Detection Module
3.4 Face Recognition Module
3.5 Social Distancing
3.6 Contact Tracing
4 Results
4.1 Face Mask Detection and Face Recognition Module
4.2 Social Distancing
4.3 Contact Tracing
5 Conclusion
References
Detection of Insider Threats Using Deep Learning: A Review
1 Introduction
2 Insider Threat
2.1 Insider Profiling
2.2 Types of Insiders
2.3 Categories of Insiders
2.4 Categories of Insider Threat
2.5 Challenges of Insider Threat
3 Insider Threat Detection Approaches
3.1 Anomaly-Based Approach
3.2 Role-Based Access Control
3.3 Scenario-Based Approach
3.4 Psychological Factors-Based Approach
4 Insider Threat Detection Approaches Using Deep Learning
4.1 Role of Deep Learning Over Machine Learning for Insider Threat Detection
4.2 Convolutional Neural Network (CNN)
4.3 Recurrent Neural Network (RNN)
4.4 Long Short-Term Memory (LSTM)
4.5 Gated Recurrent Units (GRU)
4.6 Deep Belief Network (DBN)
4.7 Autoencoder
4.8 Hybrid Neural Network
5 Datasets
6 Conclusion
References
An Incisive Analysis of Advanced Persistent Threat Detection Using Machine Learning Techniques
1 Introduction
2 Advanced Persistent Threat
2.1 Stages of Advanced Persistent Threat
3 APT Dataset—An Analysis
4 Detection Mechanism of Advanced Persistent Threat
4.1 Detection Mechanism
4.2 Defense Mechanism
4.3 Advanced Persistent Threat Detection—Challenges
5 Why Machine Learning is Used for the Detection of APT
6 Conclusion
References
Intelligent Computing Systems for Diagnosing Plant Diseases
1 Introduction
2 Literature Review
2.1 Disease Detection
2.2 Quantification
2.3 Classification
3 Requirements
4 Implementation
4.1 Techniques
4.2 Dataset
4.3 Process Flow
5 Results
6 Conclusion
References
Multimodal MRI Analysis for Segmentation of Intra-tumoral Regions of High-Grade Glioma Using VNet and WNet Based Deep Models
1 Introduction
2 Related Work
3 Data Preparation and Preprocessing
4 Method
4.1 Architecture of 2D-VNet
4.2 Architecture of Deep WNet with Residual Blocks
5 Experimentation and Results
6 Conclusion
References
Early Onset Alzheimer Disease Classification Using Convolution Neural Network
1 Introduction
2 Literature Survey
2.1 State-of-the-Art Survey
2.2 Classification Using Conventional Approaches
2.3 Classification Using Variations of CNN
2.4 Convolutional Neural Network (CNN)
3 Dataset Description and Preprocessing Mechanisms
3.1 Dataset Description
3.2 Preprocessing Mechanisms
4 Proposed CNN Architecture
5 Results
6 Conclusion
References
A Study on Evaluating the Performance of Robot Motion Using Gradient Generalized Artificial Potential Fields with Obstacles
1 Introduction
1.1 Grassfire Algorithm
1.2 Dijkstra’s Algorithm
1.3 A Algorithm
2 Methodology
2.1 Visibility Graph
2.2 Trapezoidal Decomposition
2.3 Probabilistic Road Maps
2.4 Dist Function
2.5 Handling Angular Displacement
2.6 Rapidly Exploring Random Trees
2.7 Attractive and Repulsive Artificial Potential Fields (APF)
3 Result and Discussion
4 Conclusion
References
Exploratory Analysis of Kidney Disease Data Set—A Comparative Study
1 Introduction
2 Methodology
3 Result and Discussion
3.1 Decision Tree
3.2 Random Forest
4 Conclusions
References
Deepfakes for Video Conferencing Using General Adversarial Networks (GANs) and Multilingual Voice Cloning
1 Introduction
2 Literature Survey
2.1 Generator Networks
2.2 Discriminator Networks
2.3 Translation
2.4 Generating Spectrogram Network (Voice Cloning)
3 Proposed System
3.1 Generator
3.2 Discriminator
3.3 Generating Spectrogram Network
3.4 Spectrogram Super-Resolution Network
3.5 Voice Cloning Encoder, Vocoder, Synthesizer
4 Results
5 Conclusion
References
Topic Evolution Model for Interactive Information Search
1 Introduction
1.1 Research Questions and Contributions
1.2 Materials and Methods
2 Related Works
2.1 Topic Modeling: A Fundamental Notion
2.2 Topic Evolution for Information Search
3 Topic Evolution Model for Information Search
3.1 Conceptual Framework
4 Performance Assessment and Analysis
4.1 Data and Environment Settings
4.2 Topics Generation and Visualization for User Search Interactions
4.3 Assessment of Topic Evolution Graph Generation
4.4 Accuracy Performance
4.5 Analysis
5 Conclusion
References
A Novel Automated Human Face Recognition and Temperature Detection System Using Deep Neural Networks—FRTDS
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Pre-Processing
3.2 Face Detection
3.3 Transformation of Face Data
3.4 Deep Neural Networks
3.5 Classification
3.6 Face Recognition
4 Temperature Detection
5 Integrated System—FRTDS
6 Experimental Results
6.1 System Performance Analysis
6.2 General Comparison with Existing Systems
7 Conclusion
References
A Novel BFS and CCDS-Based Efficient Sleep Scheduling Algorithm for WSN
1 Introduction
2 BFS and CCDS Scheduling
2.1 BSF Scheduling
2.2 Colored Connected Dominant Set (CCDS) Scheduling
2.3 Construction
3 Related Work
4 The Proposed Novel Sleep Scheduling Model
5 Methodology
6 Experiment
7 Results and Discussion
8 Conclusion
References
Face Recognition: A Review and Analysis
1 Introduction
2 Basics of Face Recognition System
3 Face Recognition Methods
3.1 Eigen Face
3.2 Gabor Wavelet
3.3 Artificial Neural Network (ANN)
3.4 Hidden Markov Models (HMM)
3.5 Support Vector Machine (SVM)
3.6 Deep Convolutional Neural Networks (DCNN)
4 Data Sets
4.1 FERET Data Set
4.2 AT&T Face Data Set
4.3 Yale Face Data Set
4.4 AR Data Set
4.5 LFW Data Set
5 Challenges in Face Recognition
5.1 Illumination
5.2 Pose Variation
5.3 Expression Change
5.4 Age Variation
5.5 Occlusion
5.6 Model Complexity and Parameters
6 Conclusion
References
COVID-19 Time Series Prediction and Lockdown Effectiveness
1 Introduction
2 Historic Pandemic Patterns
3 Impact on the Economy
4 Proposed Methodology
5 Determining Lockdown Effectiveness
6 Conclusion and Future Implications
References
Performance Evaluation of Electrogastrogram (EGG) Signal Compression for Telemedicine Using Various Wavelet Transform
1 Introduction
2 Materials and Methods
2.1 Electrogastrogram Acquisition
2.2 Wavelet Transform Performance Analysis
2.3 Performance Measurements
2.4 Wavelet Type and Its Performance
3 Results and Discussion
4 Conclusion
References
The Impact of UV-C Treatment on Fruits and Vegetables for Quality and Shelf Life Improvement Using Internet of Things
1 Introduction
1.1 Type of UVC Types
1.2 About IoT and Its Applications in Food Processing Industries
2 Literature Survey
3 Methodology
3.1 Plant Material and Fruits Used
3.2 Quality of Fruits and Vegetables
4 Results and Discussion
5 Conclusion
References
Modeling and Forecasting Stock Closing Prices with Hybrid Functional Link Artificial Neural Network
1 Introduction
2 FLANN
3 AEFA + FLANN-Based Forecasting
4 Experimental Results and Analysis
5 Conclusions and Future Scope
References
Whale Optimization Algorithm Based Optimal Power Flow to Reduce Generation Cost
1 Introduction
2 OPF Problem Structure
2.1 Cost Minimization
2.2 Constraints
3 Whale Optimization Algorithm
4 Simulation Results
4.1 IEEE 30 Bus System
5 Conclusion
References
An Artificial Electric Field Algorithm and Artificial Neural Network-Based Hybrid Model for Software Reliability Prediction
1 Introduction
2 ANN
3 AEFA-ANN-Based Forecasting
4 Experimental Results and Analysis
5 Conclusions
References
Disaster Event Detection from Text: A Survey
1 Introduction
2 Analysis of Disaster Event Detection from Perspective of Input Source
2.1 Social Media
2.2 News Articles
3 Disaster Event Detection Approaches
3.1 Supervised Approach
3.2 Unsupervised Approach
4 Discussion
5 Conclusion
References
Context-Adaptive Content-Based Filtering Recommender System Based on Weighted Implicit Rating Approach
1 Introduction
2 CA-CBF Recommendation Engine
2.1 Algorithm
3 Experimental Setup and Evaluation
3.1 Data Set
3.2 Methodology of Evaluation
3.3 Evaluation Metrics
3.4 Testing and Evaluation
4 Implementation Model
5 Conclusion and Future Work
References
A Deep Learning-Based Classifier for Remote Sensing Images
1 Introduction
2 Related Works
3 Methods
3.1 Dataset Used
3.2 Data Augmentation
3.3 Convolutional Neural Network
4 LeNet-5
5 Proposed CNN Model
6 Result and Analysis
6.1 Model Validation
6.2 Confusion Matrix
6.3 Accuracy of the Model
7 Conclusion and Future Work
References
Performance Evaluation of Machine Learning Algorithms to Predict Breast Cancer
1 Introduction
2 Literature Survey
3 Experimental Setup
3.1 Analysis on Dataset to Check Missing Values
3.2 Analysis of WDBC After Removing Outliers
3.3 Analysis of WDBC Dataset After Correlation
3.4 Analysis of WDBC Dataset After SMOTE
4 Comparison of All Methods
5 Conclusion
References
Topology Dependent Ant Colony-Based Routing Scheme for Software-Defined Networking in Cloud
1 Introduction
2 Literature Study
3 System Architecture
3.1 Procedure 1. AC Crossover Operation
3.2 Algorithm 1: Pseudo code of AC Crossover Operation
3.3 Procedure 2. AC Mutation Operation
3.4 Algorithm 2: Pseudo Code of AC* Mutation Operation
4 Experiments
4.1 Effectiveness of Path Computation
4.2 Network Convergence Time
4.3 End-To-End Delay
5 Conclusion
References
On Computational Complexity of Transfer Learning Approaches in Facial Analysis
1 Introduction
2 The Transfer Learning Theoretical Framework
3 Complexity Models
4 Facial Analysis Using Transfer Learning
5 Concluding Remarks
References
Adaptive Classifier Using Extreme Learning Machine for Classifying Twitter Data Streams
1 Introduction
2 Related Works
3 Adaptive Classifier System Design
3.1 System Explanation
3.2 ELM Method
4 Results and Discussion
5 Conclusion and Future Enhancement
References
Decision Making on Covid-19 Containment Zones’ Lockdown Exit Process Using Fuzzy Soft Set Model
1 Introduction
2 Definitions and Notions
3 Decision Making on Covid-19 Containment Zones’ Lockdown Exit Process Using FSS
3.1 Background of Application
3.2 Parameter Description
3.3 Proposed Algorithm
3.4 Application of the Proposed Decision-Making Algorithm for a Lockdown Exit Strategy
4 Conclusions
References
Deep Learning on Landslides: An Examination of the Potential Commitment an Expectation of Danger Evaluation in Sloping Situations
1 Introduction
2 Remote Sensing on Finding Out and Foreseeing of Landslides
3 Research on Landslide Dams Around the World:
4 Artificial Neural Networks’ Landslide Defenselessness
5 Landslide Early Warning
6 Conclusion
References
The Good, The Bad, and The Missing: A Comprehensive Study on the Rise of Machine Learning for Binary Code Analysis
1 Introduction
2 Background
3 Challenges in Existing Disassembly Tools
4 Machine Learning in Binary Program Analysis
5 Decision Making Without Human Intervention
6 Scope and Assumptions
7 Conclusions
References
Ensemble Machine Learning Approach to Detect Various Attacks in a Distributed Network of Vehicles
1 Introduction
1.1 Basics of Internet of Vehicles
1.2 Controller Area Network Bus
1.3 Different Types of Attacks
1.4 Related Work
1.5 Contribution
2 Proposed Methodology
2.1 Splitting and Mathematical Operation of XGBoost
2.2 The Working Steps of Our Proposed Model
3 Performance Evaluation
4 Conclusion
References
Predictive Analytics of Engineering and Technology Admissions
1 Introduction
2 Related Work
3 Research Methodology
3.1 Exploratory Analysis
3.2 Algorithms Used
4 Steps in Building Predictive Models Using Machine Learning
5 About the Dataset
6 Feature Engineering
7 Feature Selection
8 Experimentation
9 Result and Discussion
9.1 Model Selection for College Prediction
10 Implementation
10.1 Free Guide to Engineering Admission Aspirant Parents and Students (FGEAAPS)
11 Conclusion
References
Investigating the Impact of COVID-19 on Important Economic Indicators
1 Introduction
2 Related Work
3 Proposed Method
3.1 COVID-19 Growth Counts Derivation
4 Geopolitical and Economic Uncertainity Features
4.1 Geopolitical Risk, Economic Risk Indicator Growth Percentage Derivation
5 Experimental Results for the COVID-19 Features
6 Discussion
6.1 Geopolitical and Economic Uncertainity
7 Conclusion
8 Future Work
References
Classification of Tumorous and Non-tumorous Brain MRI Images Based on a Deep-Convolution Neural Network Model
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Image Extraction from the Dataset
3.2 Loading Labels and Pre-Image Processing
3.3 Segregating Tumorous and Non-Tumorous Dataset and Data Partitioning
3.4 Building the Convolution Neural Network Architecture
3.5 Network Training and Performance
4 Experimental Results
5 Conclusion
References
Social Distance Monitoring and Face Mask Detection Using Deep Learning
1 Introduction
2 Literature Survey
3 Method
3.1 Dataset
3.2 Training
3.3 Face Mask Detection
3.4 Social Distance Monitoring
4 Procedure
5 Experiment
5.1 Detecting Social Distance from an Input Image
5.2 Detecting Facial Mask from an Input Image
6 Results
7 Conclusion
8 Future Scope
References
An Effective VM Consolidation Mechanism by Using the Hybridization of PSO and Cuckoo Search Algorithms
1 Introduction
2 Related Works
3 Problem Formulation and Proposed System Architecture
3.1 Makespan
3.2 Energy Consumption
3.3 Fitness Function
3.4 Hybridization of Cuckoo Search and Particle Swarm Optimization Algorithm (Hybrid CSPSO)
4 Proposed VM Consolidation Algorithm by Using Hybridization of CS and PSO Algorithms
5 Simulation and Results
5.1 Evaluation of Makespan
5.2 Evaluation of Energy Consumption
6 Conclusion and Future Works
References
Customer Segmentation via Data Mining Techniques: State-of-the-Art Review
1 Introduction
2 Various Issues Involved with Customer Segmentation
3 Segmentation Techniques
3.1 Data Preparation Framework
3.2 Data Analysis Framework
4 Critical Investigation
4.1 Impact of Segmentation Variables
4.2 Model Reliability in Segmentation
4.3 Selection of Proper Data Mining Model
5 Discussion and Conclusion
References
Solar Radiation Prediction Using Artificial Neural Network: A Comprehensive Review
1 Introduction
2 ANN Models for Prediction in the Field of Solar Radiation
3 Comparative Study of the Different Techniques Used
4 Discussion and Conclusion
References
A Concise Review on Automatic Text Summarization
1 Introduction
2 Abstractive Text Summarization Techniques
2.1 NLP Architectures for Text Summarization
2.2 Word Embeddings
2.3 Structured-Based Methods
2.4 Hybrid Summarization Method
2.5 Semantic-Based Method
3 Extractive Text Summarization
4 Unsupervised Text Summarizatıon
5 Datasets for Text Summarization
6 Evaluation Methods
7 Conclusion
References
Identification of Heart Failure in Early Stages Using SMOTE-Integrated AdaBoost Framework
1 Introduction
2 Literature Study
3 Proposed Method
4 Experimental Setup
4.1 Empirical Data
4.2 Data Preprocessing
4.3 Simulation Environment and Parameter Setting
4.4 Performance Measures
5 Result Analysis
6 Conclusion
References
A Comparative Study of Different Forecasting Models for Energy Demand Forecasting
1 Introduction
2 Literature Review
3 Experimental Study
3.1 Data Source and Collection
4 Results and Analysis
5 Conclusions
References
Sentimental Analysis of Streaming COVID-19 Twitter Data on Spark-Based Framework
1 Introduction
2 Related Work
3 System Architecture
3.1 Fine-Tuned BERT Architecture
4 Methodology
4.1 Feature Selection and Engineering
4.2 Preprocessing-Label Encoding and Tokenization
4.3 Concatenation of Influential Feature
5 Experimental Setup
5.1 Dataset Description
5.2 Implementation Specification Environment
6 Results
6.1 Overall Accuracy and Loss of Fine-Tuned BERT
7 Conclusion
References
Efficient Approximate Multipliers for Neural Network Applications
1 Introduction
2 Proposed Approximate Neural Network Using Using Novel Approximate Compressor-based Booth Multiplier
2.1 Introduction to Booth Multipliers
2.2 Novel Approximate Compressor Based on k-map Alterations
3 Error Analysis of Approximate Multiplier
4 Hardware Analysis of Approximate Multiplier
5 Hardware and Error Analysis of Proposed Approximate Neural Network
6 Conclusion
References
Explainable AI (XAI) for Social Good: Leveraging AutoML to Assess and Analyze Vital Potable Water Quality Indicators
1 Introduction
2 Literature Review
3 Research Design
3.1 Data Gathering
3.2 Data Understanding
3.3 Data Pre-processing
3.4 Exploratory Data Analysis (EDA)
3.5 Model Experimentation
4 Result and Interpretation
4.1 Exploratory Data Analyses (EDA)
4.2 Model Comparison
4.3 Explainable AI (XAI)
5 Conclusion
References
Prediction of Dynamic Virtual Machine (VM) Provisioning in Cloud Computing Using Deep Learning
1 Introduction
2 Dataset
3 Methodology
3.1 Data Preprocessing
3.2 Data Formatting
3.3 Data Analysis
3.4 Model Summaries
4 Result
4.1 Root Mean Square Error (RMSE)
4.2 Mean Absolute Error (MAE)
4.3 Mean Absolute Percentage Error (MAPE)
5 Conclusion
References
Explainability of Deep Learning-Based System in Health Care
1 Introduction
2 Related Work
2.1 Foundation and Techniques of XAI
2.2 DL-Based Methods for Ocular Disease Detection
3 Proposed Approach: XAI-Based System for Ocular Disease Classification
3.1 Dataset Preparation
3.2 Model Training, Evaluation Results, and Discussion
3.3 Interpretability Method to Explain Deep Learning Model
4 Conclusion and Future Work
References
A Hybrid MSVM COVID-19 Image Classification Enhanced with Swarm Feature Optimization
1 Introduction
1.1 Sign and Symptoms
1.2 Types of COVID-19 Tests
2 Literature Review
3 Proposed Methodology
3.1 Medical Image Acquisition and Pre-processing Phase
3.2 Medical Image Feature Extraction Phase
3.3 Medical Image Hybridization Method Used for Detection Phase
4 Simulation Result Analysis
4.1 Dataset Description
4.2 Mathematical Formulas
4.3 Result Analysis
5 Conclusion and Future Scope
References
QCM Sensor-Based Alcohol Classification Using Ensembled Stacking Model
1 Introduction
2 Literature Study
3 Proposed Methodology
4 Environment Setup
4.1 Empirical Data
4.2 Performance Measure
4.3 Experimental Setup
5 Result Analysis
6 Conclusion
References
A Novel Image Falsification Detection Using Vision Transformer (Vi-T) Neural Network
1 Introduction
2 Related Work
2.1 Traditional Approach
2.2 Deep Learning Approach
3 Proposed Method
4 Experimental Test Bench
5 Results
5.1 Localization Using Attention Networks
6 Conclusion and Future Scope
References
Design of Intelligent Framework for Intrusion Detection Platform for Internet of Vehicles
1 Introduction
2 Related Works
3 Proposed Intrusion Detection System
3.1 Dataset
4 Result Analysis
4.1 Evaluation
5 Conclusion
References
Autonomous Vehicles: A Survey on Sensor Fusion, Lane Detection and Drivable Area Segmentation
1 Introduction
2 Literature Survey
2.1 Sensor Fusion
2.2 Lane Detection
2.3 Drivable Area Segmentation
3 Discussion
4 Conclusion
References
Identification of Malicious Access in IoT Network from Connection Traces by Using Light Gradient Boosting Machine
1 Introduction
2 Proposed Work
3 Simulation Results
4 Concluding Remark and Future Directions
References
Big Data in Education: Present and Future
1 Introduction
2 Applications of Big Data in Education
2.1 Prediction of Student Performance
2.2 Prediction of Student Dropout
2.3 Course Recommender System
2.4 Analyzing Learners Behavior
2.5 Construction of Curriculum
3 Critical Analysis
3.1 Research Growth of Big Data in Education
3.2 Analysis of Distinct Applications of Big Data in Education
3.3 Big Data in Education Using Various Open-Source Tools
3.4 Role of Intelligent Educational Data Mining Techniques
4 Advantages of Big Data in Education
4.1 Improvements of Student Results
4.2 Customizing Programs
4.3 Reducing Dropouts
4.4 Targeting International Recruitments
4.5 Analytics of Educators
4.6 Enhance the Learning Experience
4.7 Career Prediction
4.8 Credible Grading
5 Challenges
5.1 Limited Talent Pool
5.2 Scalability and Storage Issue
5.3 Data Errors
5.4 Data Safety Concerns
6 Future Goals
7 Conclusion
References
Breast Cancer Mammography Identification with Deep Convolutional Neural Network
1 Introduction
2 Methodology
2.1 Proposed CNN Architecture
3 Experimental Setup and Data Preparation
3.1 Dataset Overview
4 Experiment and Results
5 Conclusion
References
CatBoosting Approach for Anomaly Detection in IoT-Based Smart Home Environment
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Boosting
3.2 Cat Boosting
4 Dataset Description
4.1 Data Preprocessing
5 Result Analysis
6 Conclusion
References