TY - GEN
T1 - Multimodal mixture density boosting network for personality mining
AU - Vo, Nhi N.Y.
AU - Liu, Shaowu
AU - He, Xuezhong
AU - Xu, Guandong
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Deep learning
KW - Mixture density boosting network
KW - Personality mining
UR - http://www.scopus.com/inward/record.url?scp=85049361521&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93034-3_51
DO - 10.1007/978-3-319-93034-3_51
M3 - Conference Proceeding
AN - SCOPUS:85049361521
SN - 9783319930336
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 644
EP - 655
BT - Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
A2 - Phung, Dinh
A2 - Webb, Geoffrey I.
A2 - Ho, Bao
A2 - Tseng, Vincent S.
A2 - Ganji, Mohadeseh
A2 - Rashidi, Lida
PB - Springer Verlag
T2 - 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Y2 - 3 June 2018 through 6 June 2018
ER -