Journal of Computer Science & Systems Biology

Mohammad Ehsan Basiri

Publications

• Research Article
  Combination of Ant Colony Optimization and Bayesian Classification for Feature Selection in a Bioinformatics Dataset
  Author(s): Mehdi Hosseinzadeh Aghdam, Jafar Tanha, Ahmad Reza Naghsh-Nilchi and Mohammad Ehsan BasiriMehdi Hosseinzadeh Aghdam, Jafar Tanha, Ahmad Reza Naghsh-Nilchi and Mohammad Ehsan Basiri
  
  Feature selection is widely used as the first stage of classification task to reduce the dimension of problem, decrease noise, improve speed and relieve memory constraints by the elimination of irrelevant or redundant features. One approach in the feature selection area is employing population-based optimization algorithms such as particle swarm optimization (PSO)-based method and ant colony optimization (ACO)-based method. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. This paper empowers the ant colony optimizat.. Read More»
  DOI: 10.4172/jcsb.1000031

  Abstract PDF