This paper presents an efficient technique to prune deep and/or wide convolutional neural network models by eliminating redundant features (or filters). Previous studies have shown that over-sized deep neural network models tend to produce a lot of redundant features that are either shifted version of one another or are very similar and show little or no variations; thus resulting in filtering ...

Background: Since the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers, feature selection has been widely used in the bioinformatics field, which selects relevant features and discards irrelevant and redundant features. While redundant features contain useful information, so multi-task learning is a novel technique to improve prediction ...

In machine learning, feature selection is preprocessing step and can be effectively reduce high dimensional data, remove irrelevant data, increase learning accuracy, and improve result comprehensibility. High dimensionality of data take over efficiency and effectiveness points of view in feature selection algorithm. Efficiency stands required time to find a subset of features, and the effective...

Fast Fourier Transform (FFT) processors employed with pipeline architecture consist of series of Processing Elements (PE) or Butterfly Units (BU). BU or PE of FFT performs multiplication and addition on complex numbers. This paper proposes a single BU to compute radix-2, 8 point FFT in the time domain as well as frequency domain by replacing a series of PEs. This BU comprises of fused floating ...

In machine learning systems, especially in medical applications, clinical datasets usually contain high dimensional feature spaces with relatively few samples that lead to poor classifier performance. To overcome this problem, feature selection and ensemble classification are applied in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelev...

Predictive models benefit from a compact, non-redundant subset of features that improves interpretability and generalization. Modern data sets are wide, dirty, mixed with both numerical and categorical predictors, and may contain interactive effects that require complex models. This is a challenge for filters, wrappers, and embedded feature selection methods. We describe details of an algorithm...

In this paper we extend a recent approach for 3D object recognition in order to deal with rotationally symmetric objects, which are frequent in daily environments. We base our work in a recent method that represents objects using a hash table of shape features, which in the case of symmetric objects contains redundant information. We propose a way to remove redundant features by adding a weight...

For classification of health data, we propose in this paper a fast and accurate feature selection method, FIEBIT (Feature Inclusion and Exclusion Based on Information Theory). FIEBIT selects the most relevant and non-redundant features using Conditional Mutual Information (CMU) while excluding irrelevant and redundant features according to the comparison among Individual Symmetrical Uncertainty...

Feature Selection is to selecting the useful features from the original dataset for improve the more accurate results. Constrained Based Feature Subset Selection(CFSS) Algorithm Removes irrelevant and redundant features. This method is to find a similarity computation based on the entropy and conditional entropy values. After computing similarity computation to applied Approximate Relevancy(AR)...