Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241746" target="_blank" >RIV/61989100:27240/18:10241746 - isvavai.cz</a>

Result on the web

<a href="https://reader.elsevier.com/reader/sd/pii/S2210650216303042?token=542D17B5F4CE16DB9A9E5E17C8D447F364F9C65D9AE8A862748ADFE650DB2D83FF8B210E6955EA4F85CB77A8E011D848" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S2210650216303042?token=542D17B5F4CE16DB9A9E5E17C8D447F364F9C65D9AE8A862748ADFE650DB2D83FF8B210E6955EA4F85CB77A8E011D848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2018.02.021" target="_blank" >10.1016/j.swevo.2018.02.021</a>

Result language

angličtina

Original language name

Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach

Original language description

Selection of a representative set of features is still a crucial and challenging problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of attributes (large dimensionality); a very small number of instances or time points (small-instance set). The first situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with reasonable computational resources. The second aspect poses the problem of having insufficient data to learn from (insufficient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired by nature (in particular, by biology). We propose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classification accuracy of the data. The methods used are antlion optimization (ALO), grey wolf optimization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO).

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Name of the periodical

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

—

Volume of the periodical

42

Issue of the periodical within the volume

October

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

29-42

UT code for WoS article

000445716200003

EID of the result in the Scopus database

2-s2.0-85043393542