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This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the...
Protein features are often complex, and they are challenging to classify. In identifying the most discriminatory features in protein sequences, we propose a new feature-selection strategy by integrating the multivariate filter and Particle Swarm Optimisation (PSO) algorithms. Experimental results, based on the number of reducts and classification accuracy, were analysed in both the filter and wrapper...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
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