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

Chaehan So

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

Chaehan So

Humanities, Arts, and Social Sciences Field

Research output: Contribution to journal › Article › peer-review

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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 performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.

Original language	Undefined/Unknown
Journal	International Conference on Artificial Intelligence in Information and Communication
Publication status	Accepted/In press - 2020 Feb 29

Bibliographical note

To be published in IEEE conference proceedings: 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Access to Document

2003.01580v1

Cite this

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title = "Are You an Introvert or Extrovert?: Accurate Classification With Only Ten Predictors",

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 performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set. ",

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