John Elder

Ensemble Methods in Data Mining

Improving Accuracy Through Combining Predictions

Buch

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 over… Mehr

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Produktdetails

Weitere Autoren: Seni, Giovanni

ISBN: 978-3-031-00771-2
EAN: 9783031007712
Produktnummer: 39048676
Verlag: Springer International Publishing
Sprache: Englisch
Erscheinungsjahr: 2010
Seitenangabe: 156 S.
Masse: H27.9 cm x B21.6 cm x D0.8 cm 410 g
Abbildungen: Paperback
Gewicht: 410

Über den Autor

Giovanni Seni is a Senior Scientist with Elder Research, Inc. and directs ERI's Western office. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. Giovanni is an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class. He received a B.S. in Computer Engineering from Universidad de Los Andes (Bogota, Colombia) in 1989, and a Ph.D. in Computer Science from State University of New York at Buffalo (SUNY Buffalo) in 1995, where he studied on a Fulbright scholarship. He also holds a certificate in Data Mining and Applications from the Department of Statistics at Stanford University.Dr. John F. Elder IV heads a data mining consulting team with offices in Charlottesville, Virginia, Washington DC, and Mountain View, California (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on federal, commercial, investment, and security applications of advanced analytics, including text mining, stock selection, image recognition, biometrics, process optimization, cross-selling, drug efficacy, credit scoring, risk management, and fraud detection. ERI has become the largest and most experienced data mining consultancy. John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 15 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice's Computational & Applied Mathematics department. Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and was co-chair of the 2009 Knowledge Discovery and Data Mining conference, in Paris. His courses on analysis techniques - taught at dozens of universities, companies, and government labs - are noted for their clarity and effectiveness. John was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security. His award-winning book for practitioners of analytics, with Bob Nisbet and Gary Miner - The Handbook of Statistical Analysis & Data Mining Applications - was published in May 2009. John is a follower of Christ and the proud father of 5.