Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items.
The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a minimal performance loss (1%) achieving an accuracy of 73.81% on unseen data.
Class imbalance correction methods like SMOTE or ADASYN yielded considerable performance improvement on the cross-validation holdout set but not on unseen data. Although gradient boosting machines and random forests performed similarly on these balanced datasets, they relied on distinctly different feature importance.
Original languageEnglish
Publication statusAccepted/In press - 2020
EventInternational Conference on Artificial Intelligence in Information and Communication - Takakura Hotel, Fukuoka, Japan
Duration: 2020 Feb 192020 Feb 21
Conference number: 2
http://icaiic.org/

Conference

ConferenceInternational Conference on Artificial Intelligence in Information and Communication
Abbreviated titleICAIIC
CountryJapan
CityFukuoka
Period20/2/1920/2/21
Internet address

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