Cost Sensitive Machine Learning PDF Download
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| Author | : Balaji Krishnapuram |
| Publisher | : CRC Press |
| Total Pages | : 316 |
| Release | : 2011-12-19 |
| Genre | : Computers |
| ISBN | : 143983928X |
Download Cost-Sensitive Machine Learning Book in PDF, ePub and Kindle
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
| Author | : Jason Brownlee |
| Publisher | : Machine Learning Mastery |
| Total Pages | : 463 |
| Release | : 2020-01-14 |
| Genre | : Computers |
| ISBN | : |
Download Imbalanced Classification with Python Book in PDF, ePub and Kindle
Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Cut through the equations, Greek letters, and confusion, and discover the specialized techniques data preparation techniques, learning algorithms, and performance metrics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects.
| Author | : Longbing Cao |
| Publisher | : Springer |
| Total Pages | : 589 |
| Release | : 2010-11-18 |
| Genre | : Computers |
| ISBN | : 3642173136 |
Download Advanced Data Mining and Applications Book in PDF, ePub and Kindle
With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
| Author | : Claude Sammut |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1061 |
| Release | : 2011-03-28 |
| Genre | : Computers |
| ISBN | : 0387307680 |
Download Encyclopedia of Machine Learning Book in PDF, ePub and Kindle
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
| Author | : Alberto Fernández |
| Publisher | : Springer |
| Total Pages | : 385 |
| Release | : 2018-10-22 |
| Genre | : Computers |
| ISBN | : 3319980742 |
Download Learning from Imbalanced Data Sets Book in PDF, ePub and Kindle
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.
| Author | : Michael Todd Green |
| Publisher | : |
| Total Pages | : 96 |
| Release | : 2010 |
| Genre | : |
| ISBN | : 9781124019703 |
Download A Comparison of Methods for Learning Cost-sensitive Classiers Book in PDF, ePub and Kindle
There is a significant body of research in machine learning addressing techniques for performing classification problems where the sole objective is to minimize the error rate (i.e., the costs of misclassification are assumed to be symmetric). More recent research has proposed a variety of approaches to attacking classification problem domains where the costs of misclassification are not uniform. Many of these approaches make algorithm-specific modifications to algorithms that previously focused only on minimizing the error rate. Other approaches have resulted in general methods that transform an arbitrary error-rate focused classier into a cost-sensitive classier. While the research has demonstrated the success of many of these general approaches in improving the performance of arbitrary algorithms compared to their cost-insensitive contemporaries, there has been relatively little examination of how well they perform relative to one another. We describe and categorize three general methods of converting a cost-sensitive method into the cost-insensitive problem domain. Each method is capable of example-based cost-sensitive classification. We then present an empirical comparison of their performance when applied to the KDD98 and DMEF2 data sets. We present results showing that costing, a technique that uses the misclassification cost of individual examples to create re-weighted training data subsets, appears to outperform alternative methods when applied to DMEF2 data using increased number of re-sampled subsets. However, the performance of all methods is not statistically differentiable across either data set.
| Author | : Haibo He |
| Publisher | : John Wiley & Sons |
| Total Pages | : 222 |
| Release | : 2013-06-07 |
| Genre | : Technology & Engineering |
| ISBN | : 1118646339 |
Download Imbalanced Learning Book in PDF, ePub and Kindle
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.
| Author | : Oded Maimon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1378 |
| Release | : 2006-05-28 |
| Genre | : Computers |
| ISBN | : 038725465X |
Download Data Mining and Knowledge Discovery Handbook Book in PDF, ePub and Kindle
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
| Author | : Daria Liakh |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Predicting the Costs of Ambulatory Services Book in PDF, ePub and Kindle
| Author | : Zhi-Hua Zhou |
| Publisher | : CRC Press |
| Total Pages | : 238 |
| Release | : 2012-06-06 |
| Genre | : Business & Economics |
| ISBN | : 1439830037 |
Download Ensemble Methods Book in PDF, ePub and Kindle
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
