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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

—

Svazek periodika

42

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

29-42

Kód UT WoS článku

000445716200003

EID výsledku v databázi Scopus

2-s2.0-85043393542