Abstract The technological revolution has made available a large amount of data with many irrelevant and noisy features that alter the analysis process increase time processing. Therefore, feature selection (FS) approaches are used to select smallest subset relevant features. Feature is viewed as an optimization for which meta-heuristics have been successfully applied. Thus, in this paper, new ...

The proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing menace using a signature-based approach recent times, machine learning techniques extensively explored. This paper proposes framework combining exploit both feature selections based ...

This paper introduces novel methods for feature selec­ tion (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting method...

This paper introduces novel methods for feature selec­ tion (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting method...

This paper introduces novel methods for feature selec­ tion (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting method...

This paper introduces novel methods for feature selec­ tion (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting method...

This paper introduces novel methods for feature selec­ tion (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting method...

In the high dimensional space, problem of feature selection (FS) can be regarded as combinatorial optimization with complexity due to huge number candidate features. this article, a novel type meta-heuristic searching based on variable length solution space is proposed in order solve dimensionality issue FS and obtain more optimal results. The algorithm uses original black hole baseline for dev...

In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost) algorithm for hyperspectral image classification is first introduced and comparatively studied using diverse features. To further foster performance from both accurate efficient viewpoints, an ensemble version of GPU-CatBoost, GPU-accelerated CatBoost-Forest (GPU-CatBF) algorithm, proposed by adopting parall...

detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. in this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (mlp) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...