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2018 | OriginalPaper | Chapter

Communication Style - An Analysis from the Perspective of Automated Learning

Authors : Adriana Mihaela Coroiu, Alina Delia Călin, Maria Nuțu

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

This paper is intended to bring added value in the interdisciplinary domains of computer science and psychology, more precisely and in particular, automated learning and applied psychology. We present automated learning techniques for classification of new instances, new observations of a patient, taking into account the particularities of the attributes describing each of these observations. Specifically, information collected by applying a questionnaire for communication style (non-assertive style, manipulative style, aggressive style and assertive style) was analyzed.
Through these experiments, we have tried to determine which of the classification models are best suited to be applied in specific situations and, given the type of attributes that make up the instances of the dataset, what kind of preprocessing methods can be applied to get the most qualitative results using the selected classification models: Decision Tree Based Model, Support Vector Machine, Random Forest, Classification based on instances (k-NN), and Logistic Regression. Standard metrics were used to evaluate the performance of each of the analyzed classification patterns: accuracy, sensitivity, precision, and specificity.

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Metadata
Title
Communication Style - An Analysis from the Perspective of Automated Learning
Authors
Adriana Mihaela Coroiu
Alina Delia Călin
Maria Nuțu
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_58

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