Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00346328" target="_blank" >RIV/68407700:21230/21:00346328 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/21:00346328

Result on the web

<a href="https://doi.org/10.1109/ACCESS.2021.3052130" target="_blank" >https://doi.org/10.1109/ACCESS.2021.3052130</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3052130" target="_blank" >10.1109/ACCESS.2021.3052130</a>

Result language

angličtina

Original language name

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Original language description

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh scarcer samples that also capture interesting properties of the system. It is not known in advance which samples will be useful for model learning. Therefore, effective methods need to be employed to select informative training samples from the continuous data stream collected by the robot. Existing literature does not give any guidelines as to which of the available sample-selection methods are suitable for such a task. In this paper, we compare five sample-selection methods, including a novel method using the model prediction error. We integrate these methods into a model learning framework based on symbolic regression, which allows for learning accurate models in the form of analytic equations. Unlike the currently popular data-hungry deep learning methods, symbolic regression is able to build models even from very small training data sets. We demonstrate the approach on two real robots: the TurtleBot mobile robot and the Parrot Bebop drone. The results show that an accurate model can be constructed even from training sets as small as 24 samples. Informed sample-selection techniques based on prediction error and model variance clearly outperform uninformed methods, such as sequential or random selection.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

9

Issue of the periodical within the volume

January

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

14148-14158

UT code for WoS article

000613205000001

EID of the result in the Scopus database

2-s2.0-85099724321