Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance, Rana Dipti P., Mehta Rupa G.
Автор: Osamu Komori; Shinto Eguchi Название: Statistical Methods for Imbalanced Data in Ecology and Biology ISBN: 4431555692 ISBN-13(EAN): 9784431555698 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems.
Описание: 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.
The Handbook of Research on 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.
Автор: Salvador Garc?a; Juli?n Luengo; Francisco Herrera Название: Data Preprocessing in Data Mining ISBN: 331910246X ISBN-13(EAN): 9783319102467 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process.
Автор: Luengo Juliбn, Garcнa-Gil Diego, Ramнrez-Gallego Sergio Название: Big Data Preprocessing: Enabling Smart Data ISBN: 3030391078 ISBN-13(EAN): 9783030391072 Издательство: Springer Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
1. Introduction.- 2. Big Data: Technologies and Tools.- 3. Smart Data.- 4. Dimensionality Reduction for Big Data.- 5. Data Reduction for Big Data.- 6. Imperfect Big Data.- 7. Big Data Discretization.- 8. Imbalanced Data Preprocessing for Big Data.- 9. Big Data Software.- 10. Final Thoughts: From Big Data to Smart Data.-
Автор: Luengo Juliбn, Garcнa-Gil Diego, Ramнrez-Gallego Sergio Название: Big Data Preprocessing: Enabling Smart Data ISBN: 3030391043 ISBN-13(EAN): 9783030391041 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
1. Introduction.- 2. Big Data: Technologies and Tools.- 3. Smart Data.- 4. Dimensionality Reduction for Big Data.- 5. Data Reduction for Big Data.- 6. Imperfect Big Data.- 7. Big Data Discretization.- 8. Imbalanced Data Preprocessing for Big Data.- 9. Big Data Software.- 10. Final Thoughts: From Big Data to Smart Data.-
Описание: Are you struggling with a disparate data resource? Are there multiple existences of the same business fact scattered throughout the data resource? Are those multiple existences out of synch with each other? Do you have difficulty finding the data you need to support business activities? Do the data you find have poor quality? If the answer to any of these questions is Yes, then you need this book to guide you toward creating an integrated data resource. Most public and private sector organisations have a disparate data resource that was created over many years. That disparate data resource contains multiple existences of business facts that are out of synch with each other, are of poor quality, and are difficult to locate. The traditional approach to dealing with a disparate data resource is to perform periodic and temporary data integration to support a specific application or business activity. Those piecemeal data integration efforts may meet a current need, but seldom solve the underlying problems with a disparate data resource, and sometimes make the situation worse. This book explains how to go about understanding and resolving a disparate data resource and creating a comparate data resource that fully meets an organisations current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity. It explains the concepts, principles, and techniques for understanding a disparate data resource within the context of a common data architecture, and resolving that disparity with minimum impact on the business. Like "Data Resource Simplexity", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data resources in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, and biology, and applies them to the process of understanding and resolving a disparate data resource. He shows you how to approach and resolve a disparate data resource, and build a comparate data resource that fully supports the business.
Автор: Alberto Fern?ndez; Salvador Garc?a; Mikel Galar; R Название: Learning from Imbalanced Data Sets ISBN: 3030074463 ISBN-13(EAN): 9783030074463 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way.This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches.Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided.This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.
Автор: Fernandez Alberto, Garcia Salvador, Galar Mikel Название: Learning from Imbalanced Data Sets ISBN: 3319980734 ISBN-13(EAN): 9783319980737 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way.This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches.Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided.This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.
This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning.
The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.
Автор: Salvador Garc?a; Juli?n Luengo; Francisco Herrera Название: Data Preprocessing in Data Mining ISBN: 3319377310 ISBN-13(EAN): 9783319377315 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process.
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