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...

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

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 ...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Özellik seçimi, veri analizinde hazırlamak için uygulanan ön işlemlerden biridir. seçimi basitçe orijinal özellik kümesinden en uygun özelliklerin alt kümesinin seçim işlemidir. Bu yöntemler, setinde alakasız ve gereksiz bilgiyi belirlemeye kaldırmaya çalışır. çalışmada sınıf bilgisi kullanılarak değişim katsayısına dayalı yeni bir yöntemi önerilmiştir. Önerilen yönteminin etkinliği, gerçek set...

Data dimensionality is growing exponentially, which poses challenges to the vast majority of existing mining and learning algorithms, such as the curse of dimensionality, large storage requirement, and high computational cost. Feature selection has been proven to be an effective and efficient way to prepare high dimensional data for data mining and machine learning. The recent emergence of nove...

Many scientific disciplines use modelling and simulation processes and techniques in order to implement non-linear mapping between the input and the output variables for a given system under study. Any variable that helps to solve the problem may be considered as input. Ideally, any classifier or regressor should be able to detect important features and discard irrelevant features, and conseque...

Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions. This is an important stage of preprocessing and...