Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238744" target="_blank" >RIV/61989100:27240/17:10238744 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10238744

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S156849461630494X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S156849461630494X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2016.09.035" target="_blank" >10.1016/j.asoc.2016.09.035</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Popis výsledku v původním jazyce

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and models complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

Název v anglickém jazyce

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Popis výsledku anglicky

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and models complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

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

2017

Kód důvěrnosti údajů

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

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

52

Číslo periodika v rámci svazku

MAR

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

909-924

Kód UT WoS článku

000395896500068

EID výsledku v databázi Scopus

2-s2.0-84992080670