Constructing Models from Microarray Data with Swarm Algorithms
| dc.category | Journal Article | |
| dc.contributor.author | Vasantha Kalyani David | |
| dc.date.accessioned | 2017-03-31T01:02:46Z | |
| dc.date.available | 2017-03-31T01:02:46Z | |
| dc.date.issued | 2010 | |
| dc.department | Computer Science | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/2393 | |
| dc.lang | English | en_US |
| dc.publisher.name | International Journal of Computer Science And Information Security | en_US |
| dc.publisher.type | International | en_US |
| dc.title | Constructing Models from Microarray Data with Swarm Algorithms | en_US |
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