A Comparative Performance Study on Hybrid Swarm Model for Micro array Data
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Date
2010
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Abstract
Cancer classification based on microarray data is an important
problem. Prediction models are used for classification which helps
the diagnosis procedures to improve and aid in the physician’s
effort. A hybrid swarm model for microarray data is proposed for
performance evaluation based on Nature-inspired metaheuristic
algorithms. Firefly Algorithm (FA) is the most powerful algorithms
for optimization used for multimodal applications. In this paper a
Flexible Neural Tree (FNT) model for microarray data is
constructed using Nature-inspired algorithms. The FNT structure is
developed using the Ant Colony Optimization (ACO) and the
parameters embedded in the neural tree are optimized by Firefly
Algorithm (FA). FA is superior to the existing metaheuristic
algorithm and solves multimodal optimization problems. In this
research, comparisons are done with the proposed model for
evaluating its performance to find the appropriate model in terms of
accuracy and error rate.