About the Book
From the last two decades, researchers are looking at the imbalanced data learning as the prominent research area. The majority of critical real-world application areas like finance, health, network, news, online advertisement, social network media and weather are having the imbalanced data and emphasize the research necessary for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyberbullying identification, disaster events prediction, etc.
The machine learning algorithms are based on the heuristic of equally distributed balanced data and are providing the biased result towards the majority data class, which is not acceptable at all, as the imbalanced data is omnipresent in real life scenarios and forcing researchers to learn from imbalanced data equally for foolproof application design.
The imbalanced data is multifaceted and demands new perception to explore the knowledge using novelty at sampling approach of data preprocessing, active learning approach and cost perceptive approach to resolve data imbalance.
- Data Preparation
- Feature Engineering
- Sampling Techniques
- Granularity Management
- Sensitivity Management
- Locality Management
- Rare Itemsets Mining
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Ensemble Algorithms
- Hybrid Algorithms
- Scalable Algorithms
- Hyper Parameter Tuning
- Storage Structure: Database, Data Warehouses and Data Lakes
- Metadata Management
- Evaluation Measure
- Data Visualization
- Case Study: in the area not limited to the following + Social Network Mining + Finance + Health + Weather and Disaster Management
The aim of this book is to provide the new aspect for imbalanced data learning in an exceptional way by providing the advancement in the traditional methods of big data with the help of case study and numerous future prospects from the expertise of academia, engineering and industry.
So, the edited relevant theoretical frameworks and the latest empirical research findings help to improve the understanding the impact of imbalanced data and its resolving techniques based on the Data Preprocessing, Active Learning, and Cost Perceptive Approaches.
Target Audience are researchers, faculties, engineers, and students with a background in computer science, engineering, industry people. This book can be used as a textbook or reference book for graduate and post graduate level courses like data science, machine learning, data mining, and pattern mining to deal with the problem of learning from imbalanced data.
Also, this book can be utilized for multi disciplinary research scholars as it will provide the great source of literature to understand imbalanced data and its impact, novel approaches and the future research directions.
The book can help the beginner and intermediate researchers to re-engineer their way of thinking for the solution approach. The readers will be benefited by having the clear vision of imbalanced data characteristics and learning using out of the box solution.
Researchers and practitioners are invited to submit on or before November 11, 2020, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by November 25, 2020 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by January 24, 2021, and all interested authors must consult the guidelines for manuscript submissions at this page prior to submission.
Call for Chapters: Data Preprocessing, Active Learning & Cost Perceptive Approaches for Resolving Data Imbalance.