Sharif Digital Repository

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian  

Nanoparticles (NPs) are increasingly being used in different branches of science and in industrial applications; however, their rapid detection and characterization at low concentration levels have remained a challenge; more specifically, there is no single technique that can characterize the physicochemical properties of NPs (e.g. composition and size). In this work we have developed a colorimetric sensor array for defining the physicochemical properties of NPs in aqueous solution with ultra-low concentrations (e.g. 10-7g ml-1 for gold NPs). Various NPs were readily identified using a standard chemometric approach (i.e. hierarchical clustering analysis), with no misclassifications over 400...