Multimodal mixture density boosting network for personality mining

Nhi N.Y. Vo, Shaowu Liu, Xuezhong He, Guandong Xu*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

9 Citations (Scopus)

Abstract

Knowing people’s personalities is useful in various real-world applications, such as personnel selection. Traditionally, we have to rely on qualitative methodologies, e.g. surveys or psychology tests to determine a person’s traits. However, recent advances in machine learning have it possible to automate this process by inferring personalities from textual data. Despite of its success, text-based method ignores the facial expression and the way people speak, which can also carry important information about human characteristics. In this work, a personality mining framework is proposed to exploit all the information from videos, including visual, auditory, and textual perspectives. Using a state-of-art cascade network built on advanced gradient boosting algorithms, the result produced by our proposed methodology can achieve lower the prediction errors than most current machine learning algorithms. Our multimodal mixture density boosting network especially perform well with small sample size datasets, which is useful for learning problems in psychology fields where big data is often not available.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsDinh Phung, Geoffrey I. Webb, Bao Ho, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer Verlag
Pages644-655
Number of pages12
ISBN (Print)9783319930336
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10937 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Country/TerritoryAustralia
CityMelbourne
Period3/06/186/06/18

Keywords

  • Deep learning
  • Mixture density boosting network
  • Personality mining

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