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Special Issue "Statistical Machine Learning for Human Behaviour Analysis"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 28 February 2019

Special Issue Editors

Guest Editor
Prof. Dr. Thomas Moeslund

Visual Analysis of People Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
Website | E-Mail
Interests: computer vision; image processing; machine vision; pattern recognition; visual analysis of peoples' whereabouts; surveillance; traffic monitoring
Guest Editor
Prof. Dr. Sergio Escalera

Computer Vision Center UAB, University of Barcelona
Website | E-Mail
Phone: +34934020853
Interests: Human Behaviour Analysis; Pattern recognition; Machine Learning
Guest Editor
Prof. Dr. Gholamreza Anbarjafari

The intelligent computer vision (iCV) research lab in the Institute of Technology, University of Tartu
Website | E-Mail
Interests: Human-Robot Interaction; Emotion Recognition; Machine Learning
Guest Editor
Prof. Dr. Kamal Nasrollahi

Visual Analysis of People (VAP) Laboratory, Aalborg University, Denmark
Website | E-Mail
Interests: Action Recognition; Pattern Recognition; Machine Learning
Guest Editor
Dr. Jun Wan

The National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Science, China
Website | E-Mail
Interests: Action recognition; Human Behaviour Analysis; Pattern recognition

Special Issue Information

Dear Colleagues,

Human Behaviour Analysis has introduced a number of challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognitions. This Special Issue focuses on novel vision-based approaches, which mainly belong to broader categories, such as computer vision and machine learning. The above topics fall, mainly, under categories related to computer vision and machine learning, where the theoretical advancements and practical developments usually benefit from the contributions brought by other areas of research in the relevant domains of science and technology, which is due to the multidisciplinary nature of the task.

We solicit submissions on the following topics:

  • Information theory based pattern classification
  • Biometric recognition
  • Multimodal human analysis
  • Low resolution human activity analysis
  • Face analysis
  • Abnormal behaviour analysis
  • Unsupervised human analysis scenarios
  • 3D/4D human pose and shape estimation
  • Human analysis in virtual/augmented reality
  • Affective computing
  • Social Signal Processing
  • Personality computing
  • Activity recognition
  • Human tracking in wild
  • Application of information-theoretic concepts for human behaviour analysis

Prof. Dr. Thomas Moeslund
Prof. Dr. Sergio Escalera
Prof. Dr. Gholamreza Anbarjafari
Prof. Dr. Kamal Nasrollahi
Dr. Jun Wan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • human behaviour analysis
  • machine learning
  • information theory
  • biometrics
  • emotion recognition

Published Papers (1 paper)

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Open AccessArticle Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
Entropy 2018, 20(9), 684; https://doi.org/10.3390/e20090684
Received: 30 July 2018 / Revised: 3 September 2018 / Accepted: 6 September 2018 / Published: 7 September 2018
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The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the
[...] Read more.
The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models. Full article
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)

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