Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Editorial Introduction – Sustainable, Smart and Systemic Design Post-Anthropocene: Through a Transdisciplinary Lens
Marie Davidová, Susu Nousala, Thomas J. Marlowe
(pages: 1-10)

Systems Changes Learning: Recasting and Reifying Rhythmic Shifts for Doing, Alongside Thinking and Making
David Ing
(pages: 11-73)

Evaluating the Impact of Preconditions for Systemic Human and Non-human Communities
Susu Nousala
(pages: 74-91)

Post-Anthropocene_2.0: Alternative Scenarios through Nature/Computing Coalition Applicable in Architecture
Yannis Zavoleas
(pages: 92-120)

Applying a Systemic Approach for Sustainable Urban Hillside Landscape Design and Planning: The Case Study City of Chongqing in China
Xiao Hu, Magda Sibley, Marie Davidová
(pages: 121-153)

Rethinking Sustainability: Mapping Microclimatic Conditions on Buildings as a Regenerative Design Strategy
Ana Zimbarg
(pages: 154-172)


 

Abstracts

 


ABSTRACT


Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system.

Jacques-André Landry, Luis Da Costa, Thomas Bernier


In order to implement a multi-class object detection system, an efficient object representation is needed; in this short paper, we present a feature selection method based on the Genetic Algorithm paradigm. This method allows for the identification of a set of features that best represent the classes in the problem at hand. The idea would then be to have a broad set of features to describe any object, and then to use the presented feature selection method to adapt the description to the actual needs of the classification problem. Furthermore, the tree like solutions generated by the method can be interpreted and modified for increased generality. A brief review of literature, the first implementation of the method and the first results are presented here. The method shows potential to be used as a building block of a detection system, although further experimentation is underway in order to fully asses the power of the method.

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