Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Ghosh, Ashish (Επιμελητής έκδοσης), Dehuri, Satchidananda (Επιμελητής έκδοσης), Ghosh, Susmita (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Studies in Computational Intelligence, 98
• Θέματα: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

Πίνακας περιεχομένων:

• 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.