Evolutionary Decision Trees in Large-Scale Data Mining: 59 (Studies in Big Data, 59) - Image 1

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Springer Evolutionary Decision Trees in Large-Scale Data Mining: 59 (Studies in Big Data, 59)

Name: Evolutionary Decision Trees in Large-Scale Data Mining: 59 (Studies in Big Data, 59)
Brand: Springer
Price: 92.41 GBP
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AI & Machine Learning

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Description

This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied. Review “The structure of the book is well-thought-out. … I recommend the book for students, researchers, and developers interested in real-life applications of big data analysis.” (K. Balogh, Computing Reviews, February 15, 2021) From the Back Cover This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030218538
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 14 August 2020
Listed Since: 02 August 2020

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Springer Evolutionary Decision Trees in Large-Scale Data Mining: 59 (Studies in Big Data, 59)

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