Naslov
Machine Learning Approaches to Personality Classification on Imbalanced MBTI Datasets

Autori
Čerkez, Ninoslav ; Vareskic, Valentin

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO) / - Opatija : Institute of Electrical and Electronics Engineers (IEEE), 2021, 1259-1264

ISBN
978-953-233-101-1

Skup
44th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2021)

Mjesto i datum
Opatija, Hrvatska, 27.09.2021. - 01.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
machine learning ; classification ; imbalanced data sets ; natural language processing ; MBTI

Sažetak
The MBTI (Myers-Briggs Type Indicator) is a widely known approach to personality classification. Datasets for the machine learning approach to personality classification using MBTI are highly imbalanced. Handling imbalanced data sets is a significant open problem with a considerable impact on machine learning methods. This paper presents the results of applying different techniques and suggests their best in mitigating the challenge of imbalanced MBTI datasets. Even though most techniques could be used and implemented to some other problems and areas, like images and sound processing, natural language processing has enough challenges to focus on natural language processing and the specific issue of the MBTI datasets.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti, Psihologija, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)

Napomena
Papers in the IEEE Xplore Digital Library are indexed in ProQuest, IET databases and selectively in Compendex.

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