Multimodal mixture density boosting network for personality mining

Nhi N.Y. VO, Shaowu LIU, Xuezhong HE, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

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. Copyright © 2018 Springer International Publishing AG, part of Springer Nature.

Original languageEnglish
Title of host publicationAdvances in knowledge discovery and data mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, proceedings, part I
EditorsDinh PHUNG, Vincent S. TSENG, Geoffrey I. WEBB, Bao HO, Mohadeseh GANJI, Lida RASHIDI
Place of PublicationCham
PublisherSpringer
Pages644-655
ISBN (Electronic)9783319930343
ISBN (Print)9783319930336
DOIs
Publication statusPublished - 2018

Citation

Vo, N. N. Y., Liu, S., He, X., & Xu, G. (2018). Multimodal mixture density boosting network for personality mining. In D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji, & L. Rashidi (Eds.), Advances in knowledge discovery and data mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, proceedings, part I (pp. 644-655). Springer. https://doi.org/10.1007/978-3-319-93034-3_51

Keywords

  • Personality mining
  • Mixture density boosting network
  • Deep learning

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