ICCBR-16 Workshop
Workshop on Synergies between CBR and Knowledge Discovery
Call for Papers
At the core of CBR lies the ability of a system to learn from past cases.
However, CBR systems often incorporate knowledge discovery methods, for
example, to organize their memory or to learn adaptation rules. In turn,
knowledge discovery systems often utilize CBR as a learning methodology,
for
example, through a common set of problems with the nearest-neighbor method
and reinforcement learning. Meanwhile, the machine learning community,
which is tightly coupled with knowledge discovery, has historically
included
CBR among the types of instance-based learning.
This workshop will be dedicated to studying in-depth the possible synergies
between case-based reasoning (CBR) and knowledge discovery. It also aims at
identifying potentially fruitful ideas for co-operative problem-solving
where both CBR and knowledge discovery researchers can compare and combine
methods. In particular, new advances in knowledge discovery may help CBR to
advance its field of study and play a vital role in the future of knowledge
discovery. This first Workshop on Synergies between CBR and Knowledge
discovery aims to:
* provide a forum for identifying important contributions and
opportunities for research on combining CBR and knowledge discovery,
* promote the systematic study of how to synergistically integrate CBR
and knowledge discovery,
* showcase synergistic systems using CBR and knowledge discovery.
Some of the technical issues addressed, and potential outcomes of the
workshop, are to identify the knowledge discovery methods used in CBR, to
categorize the problems addressed by knowledge discovery in CBR, to propose
methodological improvements to fit this context’s needs, preferred types
and
methods, and guidelines to better develop CBR systems taking advantage of
all knowledge discovery research has to offer. Similarly, the workshop will
identify the CBR methods used in knowledge discovery, categorize the
problems addressed by CBR in knowledge discovery, propose methodological
improvements to fit this context’s needs, preferred types and methods, and
guidelines to better develop knowledge discovery systems taking advantage
of
all CBR research has to offer.
We welcome all those interested in the problems and promise of
synergistically combining CBR and knowledge discovery whether they belong
to
the CBR, the knowledge discovery community, or the machine learning
community.
Topics of interest include (but are not limited to):
* Architectures for synergistic systems between CBR and knowledge discovery
* Theoretical frameworks for synergistic systems between CBR and data
mining
* Memory structure mining in CBR
* Memory organization mining in CBR (decision tree induction, etc.)
* Case mining
* Feature selection in CBR
* Knowledge discovery in CBR (adaptation knowledge, meta-knowledge, etc.)
* Concept mining in CBR
* Image and multimedia mining in CBR
* Temporal mining in CBR
* Text mining in CBR
* Nearest-neighbor systems and CBR
* Instance-based learning and CBR
* Reinforcement learning and CBR
* CBR and statistics
* CBR and statistical data analysis
* CBR in multi-strategy learning systems
* CBR and similarity and metric learning
* CBR and Big Data
* CBR and deep learning
* Application specific synergies between CBR and knowledge discovery
(medicine,
bioinformatics, social networks, sentiment analysis, etc.)
Paper presentations will be interspersed with discussions in which we
characterize, categorize, and discuss the synergies between CBR and data
mining. A wrap-up round table discussion will summarize the lessons
learnt, issues identified, and future directions.
Submission Requirements
Submitted papers are limited to 10 pages in length.
All papers are to be submitted via the CBR-KD-16 EasyChair system
(https://www.easychair.org/conferences/?conf=cbrkd2016).
Papers should be in Springer LNCS format. Author's instructions, along
with LaTeX and Word macro files, are available at
http://www.springer.de/comp/lncs/authors.html.
Submissions should be original papers that have not already been published
elsewhere. However, papers may include previously published results that
support a new theme, as long as all past publications are fully referenced.
Dates
* Submission Deadline: August 12, 2016 (extended deadline)
* Notification Date: September 5, 2016
* Camera-Ready Deadline: September 25, 2016
* Workshop Date: October 31, 2016 (Atlanta, USA)
Workshop Web Site: http://cs.oswego.edu/~bichinda/iccbr2016/
Submission Site: http://www.easychair.org/conferences/?conf=cbrkd2016
Organizing Committee
Co-Chairs
Isabelle Bichindaritz
State University of New York, Oswego
Oswego, NY, 13126, USA
Phone: +1 315 312 2683
Email: ibich...@oswego.edu
Cindy Marling
Ohio University
Athens, Ohio, 45701, USA
Phone: +1 740 593 1246
Email: marl...@ohio.edu
Stefania Montani
University of Piemonte Orientale
I-15100 Alessandria, Italy
Phone: +30 0131 360158
Email: stefania.mont...@unipmn.it
--
Dr. Isabelle Bichindaritz
Associate Professor
Director of Biomedical Informatics
SUNY Oswego
Computer Science Department
Shineman 396A
7060 New York 104 Oswego, NY 13126
USA
http://cs.oswego.edu/~bichinda
Ph: (315) 312 2683
Cell: (206) 455 0221
Email: ibich...@oswego.edu
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