CFP WK on Synergies between CBR and Knowledge Discovery @ ICCBR - Extended Deadline 8/12/2016

[Isabelle Bichindaritz]

                         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: ibichind at oswego.edu

Cindy Marling
Ohio University
Athens, Ohio, 45701, USA
Phone: +1 740 593 1246
Email: marling at ohio.edu

Stefania Montani
University of Piemonte Orientale
I-15100 Alessandria, Italy
Phone: +30 0131 360158
Email: stefania.montani at 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: ibichind at oswego.edu
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