Dec 4, 2017 · For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously.

The proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and ...

Purpose The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification. Design/methodology/approach A ...

Abstract. Purpose – The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification. Design/methodology/approach ...

Fuzan Chen, Harris Wu, Runliang Dou , Minqiang Li: A high-dimensional classification approach based on class-dependent feature subspace. Ind. Manag.

The proposed method uses Matérn covariance function to describe the spatial correlation of brain regions. Additionally, PMLE is designed to model the sparsity ...

This paper presents a survey of the various subspace clustering algorithms along with a hierarchy orga- nizing the algorithms by their defining characteristics.

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or ...

In this work, an efficient feature selection method for finding and selecting informative features in high dimension data which maximum the classification ...

We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC ...