Machine Learning Approaches to Personality Classification on Imbalanced MBTI Datasets (CROSBI ID 710444)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Čerkez, Ninoslav ; Vareskic, Valentin
engleski
Machine Learning Approaches to Personality Classification on Imbalanced MBTI Datasets
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.
machine learning ; classification ; imbalanced data sets ; natural language processing ; MBTI
Papers in the IEEE Xplore Digital Library are indexed in ProQuest, IET databases and selectively in Compendex.
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Podaci o prilogu
1259-1264.
2021.
objavljeno
10.23919/mipro52101.2021.9596742
Podaci o matičnoj publikaciji
2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)
Opatija: Institute of Electrical and Electronics Engineers (IEEE)
978-953-233-101-1
2623-8764
Podaci o skupu
MIPRO 2021
predavanje
27.09.2021-01.10.2021
Opatija, Hrvatska
Povezanost rada
Informacijske i komunikacijske znanosti, Interdisciplinarne tehničke znanosti, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti), Psihologija, Računarstvo