Evolutionary Machine Learning in Linguistic Knowledge Extraction - Ahmed, Lamiaa;Badr, Amr;Abd El-Azim, Mostafa
40,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
  • Broschiertes Buch

This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets.
Autorenporträt
Dr. Lamiaa H. Ahmed is a lecturer of Computer Science at Modern Academy in Maadi, Cairo, Egypt. Computational Intelligence, Membrane Computing, Fuzzy Logic, Organic Computing and Java programming language are areas of interest.