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

IGI Global

USA

Call for Chapter

Nov 20

This job is no longer accepting applications.

Call for Chapters

Proposals Submission Deadline: November 11, 2020

Full Chapters Due: January 24, 2021

Submission Date: January 24, 2021

Introduction

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 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, 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.


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