Ensemble Methods In Data Mining
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Ensemble Methods In Data Mining
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Author : Giovanni Seni
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2010-07-07
Ensemble Methods In Data Mining written by Giovanni Seni and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-07 with Computers categories.
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensemble Complexity
Ensemble Methods In Data Mining
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Author : Giovanni Seni
language : en
Publisher: Morgan & Claypool
Release Date : 2013-07-01
Ensemble Methods In Data Mining written by Giovanni Seni and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-01 with Mathematics categories.
Ensemble Methods In Data Mining
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Author : Giovanni Seni
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Ensemble Methods In Data Mining written by Giovanni Seni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensemble Complexity
Ensemble Methods In Data Mining
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Author : Giovanni Seni
language : en
Publisher:
Release Date : 2010
Ensemble Methods In Data Mining written by Giovanni Seni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensembl ...
Recent Advances In Ensembles For Feature Selection
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Author : Verónica Bolón-Canedo
language : en
Publisher: Springer
Release Date : 2018-04-30
Recent Advances In Ensembles For Feature Selection written by Verónica Bolón-Canedo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-30 with Technology & Engineering categories.
This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.
Ensemble Methods In Data Mining
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Author : Elisa Bertino
language : en
Publisher:
Release Date : 2013
Ensemble Methods In Data Mining written by Elisa Bertino and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computer networks categories.
Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion.
Temporal Data Mining Via Unsupervised Ensemble Learning
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Author : Yun Yang
language : en
Publisher: Elsevier
Release Date : 2016-11-15
Temporal Data Mining Via Unsupervised Ensemble Learning written by Yun Yang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-15 with Computers categories.
Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. - Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks - Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches - Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view
Ensemble Methods For Machine Learning
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Author : Gautam Kunapuli
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-30
Ensemble Methods For Machine Learning written by Gautam Kunapuli and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-30 with Computers categories.
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
Ensembles In Machine Learning Applications
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Author : Oleg Okun
language : en
Publisher: Springer
Release Date : 2011-09-01
Ensembles In Machine Learning Applications written by Oleg Okun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-01 with Technology & Engineering categories.
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.
Applications Of Supervised And Unsupervised Ensemble Methods
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Author : Oleg Okun
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-10-06
Applications Of Supervised And Unsupervised Ensemble Methods written by Oleg Okun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-06 with Computers categories.
Expanding upon presentations at last year’s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.