Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature spa...

In this paper a combined feature selection method is proposed which takes advantages of sample domain filtering, resampling and feature subset evaluation methods to reduce dimensions of huge datasets and select reliable features. This method utilizes both feature space and sample domain to improve the process of feature selection and uses a combination of Chi squared with Consistency attribute ...

a robust linear quantitative structure-property relationship (qspr) model has been constructed to model and predict the refractivity indices of 101 organic compounds as common halo-derivatives of normal paraffin by application of the structural descriptors combined with multiple linear regression (mlr) method. in the main part of this study, theoretical molecular descriptors were adopted from t...

Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the a...