Titlebook: Automating the Design of Data Mining Algorithms; An Evolutionary Comp Gisele L. Pappa,Alex Freitas Book 2010 Springer-Verlag Berlin Heidelb

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Genetic Programming for Classification and Algorithm Design,, rather than classification algorithms. Although combinatorial optimization is not the focus of this book, this topic was included in this chapter because the research on automatically evolving combinatorial optimization algorithms seems to be in a more advanced stage than the research on automatic

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antecedence

Calling Children Back to Schoolassification rules from data, and it is the type of algorithm whose design is automated by the genetic programming system proposed in this book. Rule induction algorithms and their components are therefore discussed in detail in this chapter. This chapter concludes with a discussion about meta-learn

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Suzanne Gartner PhD,Yiling Liu MD, rather than classification algorithms. Although combinatorial optimization is not the focus of this book, this topic was included in this chapter because the research on automatically evolving combinatorial optimization algorithms seems to be in a more advanced stage than the research on automatic

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https://doi.org/10.1007/978-3-658-41852-6y evolved algorithms differ from the manually-designed ones; (d) we investigate the system’s sensitivity to variations in the grammar; (e) we compare the effectiveness of genetic programming and hill-climbing search as different methods for searching in the space of candidate rule induction algorith

surmount

Book 2010olutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from dat

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Diverse Education Within the Artsrning data mining, the focus is on rule induction algorithms, which have the advantage of discovering knowledge expressed in the form of . classification rules that can be easily interpreted by the user, such as: . (.=.) and (.=.) . (.=.). Concerning evolutionary computation, the focus is on genetic

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