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Titlebook: Evolutionary Decision Trees in Large-Scale Data Mining; Marek Kretowski Book 2019 Springer Nature Switzerland AG 2019 Evolutionary Computa

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書目名稱Evolutionary Decision Trees in Large-Scale Data Mining
編輯Marek Kretowski
視頻videohttp://file.papertrans.cn/318/317918/317918.mp4
概述Sums up the authors research conducted over the last 15 years on the evolutionary induction of decision trees.Discusses some basic elements from three domains are discussed, all of which are necessary
叢書名稱Studies in Big Data
圖書封面Titlebook: Evolutionary Decision Trees in Large-Scale Data Mining;  Marek Kretowski Book 2019 Springer Nature Switzerland AG 2019 Evolutionary Computa
描述.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. .
出版日期Book 2019
關(guān)鍵詞Evolutionary Computation; Decision Trees; Distributed Computing; Evolutionary Induction of Decision Tre
版次1
doihttps://doi.org/10.1007/978-3-030-21851-5
isbn_softcover978-3-030-21853-9
isbn_ebook978-3-030-21851-5Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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2197-6503 ze 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. .978-3-030-21853-9978-3-030-21851-5Series ISSN 2197-6503 Series E-ISSN 2197-6511
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https://doi.org/10.1007/978-3-030-21851-5Evolutionary Computation; Decision Trees; Distributed Computing; Evolutionary Induction of Decision Tre
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Evolutionary ComputationA lot of typical problems that have to be commonly solved in engineering or business can be formulated as optimization problems. The performance of an activity or the value of a decision are characterized by a certain cost function, and here, possible alternatives are considered.
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