Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Indexed In: SCOPUS
Release Date: June, 2021|Copyright: © 2021 |Pages: 309
DOI: 10.4018/978-1-7998-7371-6
ISBN13: 9781799873716|ISBN10: 1799873714|ISBN13 Softcover: 9781799873723|EISBN13: 9781799873730
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Description & Coverage
Description:

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance.

Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Active Learning
  • Algorithms
  • Big Data
  • Cost Perceptive Approaches
  • Data Preparation
  • Data Preprocessing
  • Data Visualization
  • Data Warehouses
  • Databases
  • Feature Engineering
  • Healthcare Systems
  • Imbalanced Data
  • Social Media
  • Spam Detection
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Editor/Author Biographies

Dipti P. Rana is working as Assistant Professor in the Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. She completed her Ph.D. in from SVNIT, Surat. She has 21+ years of experience in teaching. She delivers expert talks at national and research organizations. She supervised 15+ M. Tech. theses and currently supervising 5+ Ph.D. students. She published many papers in reputed conferences and international journals and served as reviewer in international conferences and peer reviewed journals. She published a book on “Temporal Association Rule Based Models for Weather Prediction”. Her current area of research includes Big Data Mining especially in the field of imbalanced data, health data, social network and legal data, machine learning, artificial intelligence and high performance computing.

Rupa G. Mehta is working as Associate Professor in the Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. She completed her Ph.D. in from SVNIT, Surat. She has 25+ years of experience in teaching. She delivers expert talks at national and research organizations. She supervised 15+ M. Tech. theses and currently supervising 5+ Ph.D. students. She published many papers in reputed conferences and international journals and served as reviewer in international conferences and peer reviewed journals. She published books “A Novel Approach for High Dimensional Data Clustering” and “Decision Tree Algorithms for Concept Drifted Data Stream”. Her current area of research includes Big Data Analytics, social network mining and legal data mining, machine learning and artificial intelligence.

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