Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdiscipl...
Guardat en:
| Autor principal: | |
|---|---|
| Altres autors: | , |
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Col·lecció: | Studies in Computational Intelligence
98 |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, Texte imprimé, 9783540774662 • Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, Texte imprimé, 9783642096150 • Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, Texte imprimé, 9783540846963 |
Taula de continguts:
- Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases
- Knowledge Incorporation in Multi-objective Evolutionary Algorithms
- Evolutionary Multi-objective Rule Selection for Classification Rule Mining
- Rule Extraction from Compact Pareto-optimal Neural Networks
- On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection
- Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms
- Clustering Based on Genetic Algorithms.