biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

According to a limited-resource account of feature-based attention, dividing feature-based attention by selecting targets on the basis of different features dilutes its power. Multiple-feature costs have been documented previously, but it is not clear whether the multiple-feature cost arose at the selection (segregating targets from non-targets) stage predicted by the limited-resource account. ...

Software cost estimation is one of the most crucial processes in software development management because it involves many management activities such as project planning, resource allocation and risk assessment. Accurate software cost estimation not only does help to make investment and bid plan but also enable the project to be completed in the limited cost and time. The research interest of th...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based (CFS), best first search (BFS), dominance-based rough set approach (DRSA) methods. aims to enhance classifier’s performance in decision analysis by eliminating uncorrelated inconsistent values. The proposed method, na...

The software cost prediction is a crucial element for project’s success because it helps the project managers to efficiently estimate needed effort any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, studies confirm that accurate estimations greatly depend on hyperparameter...