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...

Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. Laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. A major challenge in extracting such ...

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...

Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools improve students’ grades retention. Prediction performance a difficult process owing massive quantity educational data. Therefore, Artificial Intelligence (AI) techniques can be used for data mini...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact....

In the feature subset selection problem a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention while ignoring the rest To achieve the best possible performance with a particular learning algorithm on a particular training set a feature subset selection method should consider how the algorithm and the training set interact We e...