Basic Evaluation Scenarios for Incrementally Trained Classifiers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336159" target="_blank" >RIV/68407700:21230/19:00336159 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-30484-3_41" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-30484-3_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-30484-3_41" target="_blank" >10.1007/978-3-030-30484-3_41</a>

Result language

angličtina

Original language name

Basic Evaluation Scenarios for Incrementally Trained Classifiers

Original language description

Evaluation of incremental classification algorithms is a complex task because there are many aspects to evaluate. Besides the aspects such as accuracy and generalization that are usually evaluated in the context of classification, we also need to assess how the algorithm handles two main challenges of the incremental learning: the concept drift and the catastrophic forgetting. However, only catastrophic forgetting is evaluated by the current methodology, where the classifier is evaluated in two scenarios for class addition and expansion. We generalize the methodology by proposing two new scenarios of incremental learning for class inclusion and separation that evaluate the handling of the concept drift. We demonstrate the proposed methodology on the evaluation of three different incremental classifiers, where we show that the proposed methodology provides a more complete and finer evaluation.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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/GA18-18858S" target="_blank" >GA18-18858S: Robotic Lifelong Learning of Multi-legged Robot Locomotion Control in Autonomous Data Collection Missions</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

ISBN

978-3-030-30483-6

ISSN

0302-9743

e-ISSN

—

Number of pages

11

Pages from-to

507-517

Publisher name

Springer

Place of publication

Basel

Event location

Munich

Event date

Sep 17, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—