Constructing Models from Microarray Data with Swarm Algorithms
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Date
2010
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Abstract
Building a model plays an important role in DNA
microarray data. An essential feature of DNA microarray
data sets is that the number of input variables (genes) is far
greater than the number of samples. As such, most
classification schemes employ variable selection or feature
selection methods to pre-process DNA microarray data. In
this paper Flexible Neural Tree (FNT) model for gene
expression prordes classification is done. Based on the predefined
instruction/operator sets, a flexible neural tree
model can be created and evolved. This framework allows
input variables selection, over-layer connections and
different activation functions for the various nodes involved.
The FNT structure is developed using the Ant Colony
Optimization (ACO) and the free parameters embedded in
the neural tree are optimized by Particle Swarm
Optimization (PSO) algorithm and its enhancement (EPSO).
The purpose of this research is to find the model which is
an appropriate model for feature selection and tree-based
ensemble models that are capable of delivering high
performance classification models for microarray data.