Titlebook: Evolutionary Computation in Data Mining; Ashish Ghosh,Lakhmi C. Jain Book 2005 Springer-Verlag Berlin Heidelberg 2005 Data mining.Evolutio

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Multi-Agent Data Mining using Evolutionary Computing,gorithms that build feature-vector-based classifiers in the form of rule sets. With the tremendous explosion in the amount of data being amassed by organizations of today, it is critically important that data mining techniques are able to process such data efficiently. We present the Distributed Lea

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Vital-Signs

Diversity and Neuro-Ensemble,ns. It has been shown that combining different neural networks can improve the generalization ability of learning machines. Diversity of the ensemble’s members plays a key role in minimizing the combined bias and variance of the ensemble. In this chapter, we compare between different mechanisms and

GNAT

Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets,ionary techniques have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Hence, they can improve clustering. However, existing . clustering techniques suffer from one or more of the following shortcomings: (i) they are

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dictator

Microarray Data Mining with Evolutionary Computation,umber of gene expressions coupled with analysis over a time course, provides an immense space of possible relations. Some small portion of this space contains information that is of extreme value to modern biomedicine in terms of proper diagnosis and treatment of many diseases. Classical methods of

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https://doi.org/10.1057/9780230306851ion systems from the view point of its components. Then we propose a decompositional rule extraction method based on RBF neural networks. In the proposed rule extraction method, rules are extracted from trained RBF neural networks with class-dependent features. GA is used to determine the feature su