Reflecting on Imbalance Data Issue when Teaching Performance Measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39902712" target="_blank" >RIV/00216275:25530/17:39902712 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-57261-1_4" target="_blank" >http://dx.doi.org/10.1007/978-3-319-57261-1_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-57261-1_4" target="_blank" >10.1007/978-3-319-57261-1_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reflecting on Imbalance Data Issue when Teaching Performance Measures

Popis výsledku v původním jazyce

Importance of soft computing methods has continuously grown for many years. Particularly machine learning methods have been paid considerable attention in the business sphere and subsequently within the general public in the last decade. Machine learning and its implementation is the object of interest of many commercial subjects, whether they are small companies or large corporations. Consequently, well-educated experts in the area of machine learning are highly sought after on the job market. Most of the technical universities around the world have incorporated the machine learning into their curricula. However, machine learning is a dynamically evolving area and the curricula should be continuously updated. This paper is intended to support this process. Namely, an imbalance data issue, in context of performance measures for binary classification, is opened, and a teaching method covering this problem is presented. The method has been primary designed for undergraduate and graduate students of technical fields; however, it can be easily adopted in curricula of other fields of study, e.g. medicine, economics, or social sciences.

Název v anglickém jazyce

Reflecting on Imbalance Data Issue when Teaching Performance Measures

Popis výsledku anglicky

Importance of soft computing methods has continuously grown for many years. Particularly machine learning methods have been paid considerable attention in the business sphere and subsequently within the general public in the last decade. Machine learning and its implementation is the object of interest of many commercial subjects, whether they are small companies or large corporations. Consequently, well-educated experts in the area of machine learning are highly sought after on the job market. Most of the technical universities around the world have incorporated the machine learning into their curricula. However, machine learning is a dynamically evolving area and the curricula should be continuously updated. This paper is intended to support this process. Namely, an imbalance data issue, in context of performance measures for binary classification, is opened, and a teaching method covering this problem is presented. The method has been primary designed for undergraduate and graduate students of technical fields; however, it can be easily adopted in curricula of other fields of study, e.g. medicine, economics, or social sciences.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50302 - Education, special (to gifted persons, those with learning disabilities)

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 statě ve sborníku

Artificial Intelligence Trends in Intelligent Systems : Proceedings of the 6th Computer Science On-line Conference 2017 (CSOC2017). Vol 1

ISBN

978-3-319-57261-1

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

10

Strana od-do

33-42

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Zlín

Datum konání akce

26. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—