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

Dipti Rana (SVNIT, Surat, India) and Rupa Mehta (SVNIT, Surat, India)
Pages: 300|DOI: 10.4018/978-1-7998-7371-6
ISBN13: 9781799873716|ISBN10: 1799873714|EISBN13: 9781799873730|ISBN13 Softcover: 9781799873723


Over the last two decades, researchers are looking at imbalanced data learning as a 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 imbalanced data and this 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. 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 is forcing us to learn from imbalanced data for foolproof application design. The imbalanced data is multifaceted and demands a new perception to explore the knowledge using the novelty at sampling approach of data preprocessing, an active learning approach and a cost perceptive approach to resolve data imbalance. The book offers new aspects for imbalanced data learning in an exceptional way by providing the advancements of the traditional methods with respect to big data through case studies and research from expertise in academia, engineering and industry. The book can help re-engineer the way of thinking for the solution approach. The readers will be benefited by having a clear vision of imbalanced data characteristics and learning using out of the box solutions.

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