Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree

“…For instance, in the study of Abdi and Giveki (13) and in the study of Xie, Xie (8), ensemble of SVM and other methods gained a better accuracy than SVM alone.…”

An Optimized Feature Subset Selection Method for Network Flows based on Machine Learning

“…Xie included a feature ranking in a sequential forward search method with the application of the F-score measure to rank the features [5] , while Peng added a random sampling method to choose features from the ranking . Zhang and Bonilla-Huerta proposed the similar methods including a Relieff estimation based ranking, which were also applied to compress the searching space.…”

Identification of Erythemato-Squamous Skin Diseases Using Support Vector Machines and Extreme Learning Machines: A Comparative Study towards Effective Diagnosis

“…In [2], Xie and Wang developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. They proposed an improved hybrid feature selection method, named improved F-score and Sequential Forward Search (IFSFS), which is a combination of filter and wrapper methods to select the optimal feature subset from the original feature set.…”

“…Although the diagnosis of erythemato-squamous diseases have recently caught the attention of AI researchers leading to the use of some AI techniques [1][2][3][4], no one has yet implemented extreme learning machines in the identification of these diseases and compared its performance with support vector machines.…”