Recognition of Personality Traits using Meta Classifiers

Firoj Alam; Giuseppe Riccardi; Shammur Chowdhury

Recognition of Personality Traits using Meta Classifiers

Firoj Alam, Giuseppe Riccardi, Shammur Chowdhury

University of Trento

Research output: Book/Report › Commissioned report › peer-review

Abstract

In this paper, we have tried to understand human personality traits by using meta classifiers. We have used SMO (Sequential Minimal Optimization for Support Vector Machine), RF (Random Forest) and Adaboost as the three main algorithms to design our meta classifiers. As a method of evaluation, we have used weighted and un-weighted average evaluation measure according to the Interspeech 2012 speaker traits challenge guidelines. In the Interspeech 2012 speaker traits challenge, the organizer provided Speaker Personality Corpus (SPC), which we used to design our meta classifiers and measured the accuracy of the system.

Original language	English
Publication status	Published - 2012
Externally published	Yes

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Cite this

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title = "Recognition of Personality Traits using Meta Classifiers",

abstract = "In this paper, we have tried to understand human personality traits by using meta classifiers. We have used SMO (Sequential Minimal Optimization for Support Vector Machine), RF (Random Forest) and Adaboost as the three main algorithms to design our meta classifiers. As a method of evaluation, we have used weighted and un-weighted average evaluation measure according to the Interspeech 2012 speaker traits challenge guidelines. In the Interspeech 2012 speaker traits challenge, the organizer provided Speaker Personality Corpus (SPC), which we used to design our meta classifiers and measured the accuracy of the system.",

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T1 - Recognition of Personality Traits using Meta Classifiers

AU - Alam, Firoj

AU - Riccardi, Giuseppe

AU - Chowdhury, Shammur

PY - 2012

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AB - In this paper, we have tried to understand human personality traits by using meta classifiers. We have used SMO (Sequential Minimal Optimization for Support Vector Machine), RF (Random Forest) and Adaboost as the three main algorithms to design our meta classifiers. As a method of evaluation, we have used weighted and un-weighted average evaluation measure according to the Interspeech 2012 speaker traits challenge guidelines. In the Interspeech 2012 speaker traits challenge, the organizer provided Speaker Personality Corpus (SPC), which we used to design our meta classifiers and measured the accuracy of the system.

M3 - Commissioned report

BT - Recognition of Personality Traits using Meta Classifiers

ER -

Recognition of Personality Traits using Meta Classifiers

Abstract

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