Cargando…
Automating the Design of Data Mining Algorithms An Evolutionary Computation Approach /
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, governme...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
|
| Edición: | 1st ed. 2010. |
| Colección: | Natural Computing Series,
|
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-02541-9 | ||
| 003 | DE-He213 | ||
| 005 | 20230719191937.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2010 gw | s |||| 0|eng d | ||
| 020 | |a 9783642025419 |9 978-3-642-02541-9 | ||
| 024 | 7 | |a 10.1007/978-3-642-02541-9 |2 doi | |
| 050 | 4 | |a QA76.9.D343 | |
| 072 | 7 | |a UNF |2 bicssc | |
| 072 | 7 | |a UYQE |2 bicssc | |
| 072 | 7 | |a COM021030 |2 bisacsh | |
| 072 | 7 | |a UNF |2 thema | |
| 072 | 7 | |a UYQE |2 thema | |
| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Pappa, Gisele L. |e author. |0 (orcid)0000-0002-0349-4494 |1 https://orcid.org/0000-0002-0349-4494 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Automating the Design of Data Mining Algorithms |h [electronic resource] : |b An Evolutionary Computation Approach / |c by Gisele L. Pappa, Alex Freitas. |
| 250 | |a 1st ed. 2010. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2010. | |
| 300 | |a XIII, 187 p. 33 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Natural Computing Series, |x 2627-6461 | |
| 505 | 0 | |a Data Mining -- Evolutionary Algorithms -- Genetic Programming for Classification and Algorithm Design -- Automating the Design of Rule Induction Algorithms -- Computational Results on the Automatic Design of Full Rule Induction Algorithms -- Directions for Future Research on the Automatic Design of Data Mining Algorithms. | |
| 520 | |a Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary 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 data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Artificial intelligence-Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Data Science. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 700 | 1 | |a Freitas, Alex. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642025426 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642025402 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642261251 |
| 830 | 0 | |a Natural Computing Series, |x 2627-6461 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-02541-9 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||