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.
Call for Papers
- 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
- Final Deadline to submit proposal: December 25, 2020
- Notification of Acceptance: December 31, 2020
- Full Chapter Submission: January 24, 2021
- Review Results Returned: March 9, 2021
- Final Acceptance Notification: April 20, 2021
- Final Chapter Submission: May 4, 2021
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 multidisciplinary 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.