Optimal column subset selection for image classification by genetic algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099100" target="_blank" >RIV/61989100:27240/16:86099100 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s10479-016-2331-0" target="_blank" >http://dx.doi.org/10.1007/s10479-016-2331-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-016-2331-0" target="_blank" >10.1007/s10479-016-2331-0</a>

Result language

angličtina

Original language name

Optimal column subset selection for image classification by genetic algorithms

Original language description

Many problems in operations research can be solved by combinatorial optimization. Fixed-length subset selection is a family of combinatorial optimization problems that involve selection of a set of unique objects from a larger superset. Feature selection, p-median problem, and column subset selection problem are three examples of hard problems that involve search for fixed-length subsets. Due to their high complexity, exact algorithms are often infeasible to solve real-world instances of these problems and approximate methods based on various heuristic and metaheuristic (e.g. nature-inspired) approaches are often employed. Selecting column subsets from massive data matrices is an important technique useful for construction of compressed representations and low rank approximations of high-dimensional data. Search for an optimal subset of exactly k columns of a matrix, (Formula presented.), (Formula presented.), is a well-known hard optimization problem with practical implications for data processing and mining. It can be used for unsupervised feature selection, dimensionality reduction, data visualization, and so on. A compressed representation of raw real-world data can contribute, for example, to reduction of algorithm training times in supervised learning, to elimination of overfitting in classification and regression, to facilitation of better data understanding, and to many other benefits. This paper proposes a novel genetic algorithm for the column subset selection problem and evaluates it in a series of computational experiments with image classification. The evaluation shows that the proposed modifications improve the results obtained by artificial evolution. (C) 2016 Springer Science+Business Media New York

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

—

Project

<a href="/en/project/GJ16-25694Y" target="_blank" >GJ16-25694Y: Multi-paradigm data mining algorithms based on information retrieval, fuzzy, and bio-inspired methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

—

Volume of the periodical

2016

Issue of the periodical within the volume

2016

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-84991018113