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Full-Text Articles in Physical Sciences and Mathematics
Hyperspectral Data Processing In A High Performance Computing Environment: A Parallel Best Band Selection Algorithm, Stefan Robila, Gerald Busardo
Hyperspectral Data Processing In A High Performance Computing Environment: A Parallel Best Band Selection Algorithm, Stefan Robila, Gerald Busardo
Department of Computer Science Faculty Scholarship and Creative Works
Hyperspectral data are characterized by a richness of information unique among various visual representations of a scene by representing the information in a collection of grayscale images with each image corresponding to a narrow interval in the electromagnetic spectrum. Such detail allows for precise identification of materials in the scene and promises to support advances in imaging beyond the visible range. However, hyperspectral data are considerably large and cumbersome to process and efficient computing solutions based on high performance computing are needed. In this paper we first provide an overview of hyperspectral data and the current state of the art …
Study Of Feature Selection Algorithms For Text-Categorization, Kandarp Dave
Study Of Feature Selection Algorithms For Text-Categorization, Kandarp Dave
UNLV Theses, Dissertations, Professional Papers, and Capstones
This thesis will discuss feature selection algorithms for text-categorization. Feature selection algorithms are very important, as they can make-or-break a categorization engine. The feature selection algorithms that will be discussed in this thesis are Document Frequency, Information Gain, Chi Squared, Mutual Information, NGL (Ng-Goh-Low) coefficient, and GSS (Galavotti-Sebastiani-Simi) coefficient . The general idea of any feature selection algorithm is to determine importance of words using some measure that can keep informative words, and remove non-informative words, which can then help the text-categorization engine categorize a document, D , into some category, C . These feature selection methods are explained, implemented, …
Feature Selection For Classification Using An Ant Colony System, Nadia Abd-Alsabour, Marcus Randall
Feature Selection For Classification Using An Ant Colony System, Nadia Abd-Alsabour, Marcus Randall
Marcus Randall
Many applications such as pattern recognition require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant or redundant features while keeping the most informative ones. In this paper, an ant colony system approach for solving feature selection for classification is presented. The proposed algorithm was tested rising artificial and real-world datasets. The results are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets. The results of the proposed algorithm have been compared with …
A Logic Method For Efficient Reduction Of The Space Complexity Of The Attribute Reduction Problem, Mehmet Hacibeyoğlu, Fati̇h Başçi̇ftçi̇, Şi̇rzat Kahramanli
A Logic Method For Efficient Reduction Of The Space Complexity Of The Attribute Reduction Problem, Mehmet Hacibeyoğlu, Fati̇h Başçi̇ftçi̇, Şi̇rzat Kahramanli
Turkish Journal of Electrical Engineering and Computer Sciences
The goal of attribute reduction is to find a minimal subset (MS) R of the condition attribute set C of a dataset such that R has the same classification power as C. It was proved that the number of MSs for a dataset with n attributes may be as large as (_{n/2}^n) and the generation of all of them is an NP-hard problem. The main reason for this is the intractable space complexity of the conversion of the discernibility function (DF) of a dataset to the disjunctive normal form (DNF). Our analysis of many DF-to-DNF conversion processes showed that approximately …