Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...

conclusions test-retest and correlation analyses have identified non-redundant radiomics features and this feature are prone to errors if they employed as quantitative biomarker for gbm image analysis. however when we use robust and redundant feature, quantitative image radiomics features are informative and prognostic biomarkers for gbm magnetic resonance imaging. results results shows that th...

The Pseudo Fisher Linear Discriminant (PFLD) based on a pseudo-inverse technique shows a peaking behaviour of the generalization error for training sample sizes that are about the feature size: with an increase in the training sample size the generalization error at first decreases reaching the minimum, then increases reaching the maximum at the point where the training sample size is equal to ...

Responses in auditory cortex tend to be weaker, more phasic, and noisier than those of auditory brainstem and midbrain nuclei. Is the activity in cortex therefore merely a "degraded echo" of lower-level neural representations? In this issue of Neuron, Chechik and colleagues show that, while cortical responses indeed convey less sensory information than auditory midbrain neurons, their responses...

workspace analysis is one of the most important issues in the robotic parallel manipulator design. ‎however, ‎the unidirectional constraint imposed by cables causes this analysis more challenging in the cabledriven redundant parallel manipulators. controllable workspace is one of the general workspace in the cabledriven redundant parallel manipulators due to the dependency on geometry parameter...

reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. it also decreases memory and transmission bandwidth requirements. moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. in this paper, the kohene...

Feature extraction is the process of eliminating the irrelevant information and redundant features during Data Mining. Of all the existing feature subset selection algorithms, most of them can effectively eliminate irrelevant features but fail to handle redundant features. The Improved FAST eliminates irrelevant features first and from the result set it removes the redundant features. The Impro...