By Carlos Coello Coello, Satchidananda Dehuri, Susmita Ghosh
The function of this publication is to gather contributions which are on the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and knowledge mining. any such assortment intends to demonstrate the potential for multi-objective swarm intelligence suggestions in facts mining, with the purpose of motivating extra researchers in evolutionary computation and laptop studying to do examine during this box.
This quantity includes 11 chapters, together with an advent that offers the elemental strategies of swarm intelligence thoughts and a dialogue in their use in information mining. a number of the examine demanding situations that needs to be confronted while utilizing swarm intelligence concepts in information mining also are addressed. the remainder of the chapters have been contributed by way of best researchers, and have been geared up in accordance with the stairs ordinarily in wisdom Discovery in Databases (KDD) (i.e., information preprocessing, facts mining, and put up processing).
We wish that this booklet turns into a precious reference for these wishing to do learn at the use of multi-objective swarm intelligence innovations in information mining and information discovery in databases.
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In: Proceedings of the 2003 UK Workshop on Computational Intelligence, Bristol, UK, September 2003, pp. : Fuzzy-rough attribute reduction with application to web categorization. : Fuzzy-rough data reduction with ant colony optimization. : Fuzzy Sets and Fuzzy Logic: theory and applications. : A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. International Joint Conf. : Toward integrating feature selection algorithms for classification and clustering.
436–441. : The application of the ant colony decision rule algorithm on distributed data mining. : A new hybrid approach for mining breastcancer pattern using discrete particle swarm optimization and statistical method. : A hybrid particle swarm optimizationback-propagation algorithm for feed-forward neural network training. Applied Mathematics and Computation 185, 1026–1037 (2007) 2 Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers Susana M. C. Sousa1 , and Thomas A. Runkler2 1 2 Center of Intelligent Systems - IDMEC Instituto Superior T´ecnico Technical University of Lisbon Av.
16) The number of nodes Nf (k) that each ant k has to visit on each tour t is only updated every In tours (iterations), in order to allow the search for the best features for each features cardinality Nf . The algorithm runs I times. 10). 17) with j = 1, . . , n. For the features cardinality (first colony), the heuristic value is computed using the Fisher discriminant criterion for feature selection [12]. 18) where μ1 (i) and μ2 (i) are the mean values of feature i for the samples in class 1 and class 2, and σ12 and σ22 are the variances of feature i for the samples in class 1 and 2.