Deadline approaching: IWLCS @ GECCO (March 28)

Posted on March 15, 2013 by docurbs

Just a quick reminder that the deadline for the IWLCS will be here in two weeks (March 28).  IWLCS is a great place to present your quality projects and ongoing work related to LCS research.  This year it is particularly important for you to make a contribution to this workshop which serves as a valuable and central exchange of knowledge and ideas for those interested in the study of these unique algorithms, as well as for those interested in learning more about them.

Last year the IWLCS only got two paper submissions, a record low for the meeting.  It is our hope that we will see many more submissions this year in order to demonstrate interest in this workshop.

Submission instructions are on the IWLCS 2013 website under the CFP tab.

http://homepages.ecs.vuw.ac.nz/~iqbal/iwlcs2013/index.html
*********************************************************************
**                            CALL FOR PAPERS                      **
** Sixteenth International Workshop on Learning Classifier Systems **
**                 July 06-10, 2013, Amsterdam, The Netherlands    **
**                       Organized by ACM SIGEVO                   **
*********************************************************************

The Sixteenth International Workshop on Learning Classifier Systems (IWLCS
2013) will be held in Amsterdam, The Netherlands during the Genetic and
Evolutionary Computation Conference (GECCO-2013), July 06-10, 2013.

Originally, Learning Classifier Systems (LCSs) were introduced by John H.
Holland as a way of applying evolutionary computation to machine learning
and adaptive behaviour problems. Since then, the LCS paradigm has
broadened greatly into a framework that encompasses many representations,
rule discovery mechanisms, and credit assignment schemes.

Current LCS applications range from data mining, to automated innovation
and the on-line control of cognitive systems. LCS research includes
various actual system approaches: While Wilson’s accuracy-based XCS system
(1995) has received the highest attention and gained the highest
reputation; studies and developments of other LCSs are usually discussed
and contrasted. Advances in machine learning, and reinforcement learning
in particular, as well as in evolutionary computation have brought LCS
systems the necessary competence and guaranteed learning properties. Novel
insights in machine learning and evolutionary computation are being
integrated into the LCS framework.

Thus, we invite submissions that discuss recent developments in all areas
of research on, and applications of, Learning Classifier Systems. IWLCS is
the event that brings together most of the core researchers in classifier
systems. The workshop also provides an opportunity for researchers
interested in LCSs to get an impression of the current research directions
in the field as well as a guideline for the application of LCSs to their
problem domain.

Topics of interests include but are not limited to:

Paradigms of LCS (Michigan, Pittsburgh …)
Theoretical developments (behaviour, scalability and learning bounds …)
Representations (binary, real-valued, oblique, non-linear, fuzzy …)
Types of target problems (single-step, multiple-step, regression/function
approximation …)
System enhancements (competent operators, problem structure identification
and linkage learning …)
LCS for Cognitive Control (architectures, emergent behaviours …)
Applications (data mining, medical domains, bioinformatics …)
Optimizations and parallel implementations (GPU, matching algorithms …)

All accepted papers will be presented at IWLCS 2013 and will appear in the
GECCO workshop volume, which will be published by ACM (Association for
Computing Machinery). Authors will be invited after the workshop to submit
revised (full) papers that, after a thorough review process, are to be
published in a special issue of the Evolutionary Intelligence journal.

Important dates

March 28, 2013   – Paper submission deadline
April 15, 2013   – Notification to authors
April 25, 2013   – Submission of camera-ready material
July 06-10, 2013 – GECCO 2013 Conference in Amsterdam, The Netherlands

Organizing Committee

Muhammad Iqbal, muhammad.iqbal@ecs.vuw.ac.nz
Kamran Shafi, k.shafi@adfa.edu.au
Ryan Urbanowicz, ryan.j.urbanowicz@dartmouth.edu

 

Regards

Ryan Urbanowicz

 

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2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2013)

Posted on December 7, 2012 by will

*** CALL FOR PAPERS ***
2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2013) *** Genetics-Based Machine Learning track ***
*** July 06-10, 2013, Amsterdam, The Netherlands ***
*** Organized by ACM SIGEVO ***
***http://www.sigevo.org/gecco-2013 ***

The Genetics-Based Machine Learning (GBML) track at GECCO 2013 covers all advances in theory and application of evolutionary computation methods to Machine Learning (ML) problems.

ML presents an array of paradigms — unsupervised, semi-supervised, supervised, and reinforcement learning — which frame a wide range of clustering, classification, regression, prediction and control tasks.

The literature shows that evolutionary methods can tackle many different tasks within the ML context:

- addressing subproblems of ML e.g. feature selection and construction
- optimising parameters of other ML methods
- as learning methods for classification, regression or control tasks
- as meta-learners which adapt base learners
* evolving the structure and weights of neural networks
* evolving the data base and rule base in genetic fuzzy systems
* evolving ensembles of base learners

The global search performed by evolutionary methods can complement the local search of non-evolutionary methods and combinations of the two are particularly welcome.

Some of the main GBML subfields are:

* Learning Classifier Systems (LCS) are rule-based systems introduced
by John Holland in the 1970s. LCSs are one of the most active and
best-developed forms of GBML and we welcome all work on them.
* Genetic Programming (GP) when applied to machine learning tasks (as
opposed to function optimisation).
* Evolutionary ensembles, in which evolution generates a set of
learners which jointly solve problems.
* Artificial Immune Systems (AIS).
* Evolving neural networks or Neuroevolution.
* Genetic Fuzzy Systems (GFS) which combine evolution and fuzzy logic.

In addition we encourage submissions including but not limited to the
following:

1. Theoretical advances

* Theoretical analysis of mechanisms and systems
* Identification and modeling of learning and scalability bounds
* Connections and combinations with machine learning theory
* Analysis and robustness in stochastic, noisy, or non-stationary
environments
* Complexity analysis in MDP and POMDP problems
* Efficient algorithms

2. Modification of algorithms and new algorithms

* Evolutionary rule learning, including but not limited to:
o Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS…)
o Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE,
MOLCS, GAssist…)
o Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS…)
o Iterative Rule Learning Approach (SIA, HIDER, NAX, BioHEL,…)
* Artificial Immune Systems
* Genetic fuzzy systems
* Learning using evolutionary Estimation of Distribution
Algorithms (EDAs)
* Evolution of Neural Networks
* Evolution of ensemble systems
* Other hybrids combining evolutionary techniques with other
machine learning techniques

3. Issues in GBML

* Competent operator design and implementation
* Encapsulation and niching techniques
* Hierarchical architectures
* Default hierarchies
* Knowledge representations, extraction and inference
* Data sampling
* (Sub-)Structure (building block) identification and linkage learning
* Integration of other machine learning techniques
* Mechanisms to improve scalability

4. Applications

* Data mining
* Bioinformatics and life sciences
* Rapid application development frameworks for GBML
* Robotics, engineering, hardware/software design, and control
* Cognitive systems and cognitive modeling
* Dynamic environments, time series and sequence learning
* Artificial Life
* Adaptive behavior
* Economic modelling
* Network security
* Other kinds of real-world applications

5. Related Activities

* Visualisation of all aspects of GBML (performance, final solutions, evolution of the population)
* Platforms for GBML, e.g. GPGPUs
* Competitive performance, e.g. GBML performance in Competitions and Awards
* Education and dissemination of GBML, e.g. software for teaching and exploring aspects of GBML.

All accepted papers will appear in the proceedings of GECCO 2013, which will be published by ACM (Association for Computing Machinery).

Important Dates:

January 23, 2013 – Paper submission deadline
April 17, 2013 – Camera-ready version of accepted articles
July 06-10, 2013 – GECCO 2013 Conference in Amsterdam, The Netherlands

Track Chairs:
- Jaume Bacardit,jaume.bacardit@nottingham.ac.uk
- Tim Kovacs,kovacs@cs.bris.ac.uk

Posted in ACM SIGEVO, announcements, Call for papers, CFP, GECCO, SIGEVO | Tagged | Leave a comment

GECCO 2012 Genetics-based Machine Learning track deadline extended to 27 Jan. 2012

Posted on January 11, 2012 by will

The submission deadline for all tracks at GECCO 2012 has been extended to January 27, 2012

====================================================================
GECCO 2012: Call for Papers on GENETICS-BASED MACHINE LEARNING (GBML)

2012 Genetic and Evolutionary Computation Conference (GECCO-2012)
The largest conference in the field of evolutionary computation
July 7-11, Philadelphia, USA
http://www.sigevo.org/gecco-2012/

**Extended submission deadline: January 27, 2012**

Co-located with the International Workshop on Learning Classifier Systems (IWLCS)
====================================================================

The Genetics-Based Machine Learning (GBML) track at GECCO covers all advances in theory and application of evolutionary computation methods to Machine Learning (ML) problems.

ML presents an array of paradigms — unsupervised, semi-supervised, supervised, and reinforcement learning — which frame a wide range of clustering, classification, regression, prediction and control tasks.

Evolutionary methods have a range of uses in ML:
- addressing subproblems of ML e.g.
– feature selection and construction
– optimising parameters of other ML methods
- as learning methods e.g.
– generating classification hypotheses with Genetic Programming
– learning control systems or cognitive modelling with Learning Classifier Systems
- as meta-learners which adapt base learners e.g.
– evolving the structure and weights of neural networks
– evolving the data base and rule base in genetic fuzzy systems
– evolving ensembles of base learners
– evolving representations, update rules or algorithms for base learners

The global search performed by evolutionary methods can complement the local search of non-evolutionary methods and combinations of the two are particularly welcome.

Free tutorials include:
- Learning Classifier Systems
- Large Scale Data Mining using Genetics-Based Machine Learning

Track Chairs

Dr. Will Browne, Victoria University of Wellington, NZ (will.browne -at-
ecs -dot- vuw -dot- ac -dot- nz)

Dr. Tim Kovacs, University of Bristol, U.K. (kovacs -at- cs -dot- bris
-dot- ac -dot- uk)

Posted in Conferences, SIGEVO | Tagged , | Leave a comment

OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

Posted on October 28, 2011 by Community

Abstract  
The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information
through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean
continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which
constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous
attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while
a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions.
After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-16
  • DOI 10.1007/s00500-011-0778-0
  • Authors
    • Jose M. Cadenas, Dept. de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • M. Carmen Garrido, Dept. de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • Raquel Martínez, Dept. de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • Piero P. Bonissone, GE Global Research, One Research Circle, Niskayuna, NY 12309, USA
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How landscape ruggedness influences the performance of real-coded algorithms: a comparative study

Posted on October 15, 2011 by Community

Abstract  
Ruggedness has a strong influence on the performance of algorithms, but it has been barely studied in real-coded optimization,
mainly because of the difficulty of isolating it from a number of involved topological properties. In this paper, we propose
a framework consisting of increasing ruggedness function sets built by a mechanism which generates multiple funnels. This
mechanism introduces different levels of sinusoidal distortion which can be controlled to isolate the singular influence of
some related features. Some commonly used measures of ruggedness have been applied to analyze these sets of functions, and
a numerical study to compare the performance of some representative algorithms has been carried out. The results confirm that
ruggedness has an influence on the performance of the algorithm, proving that it depends on the multi-funnel structure and
peak features, such as height and relative size of the global peak, and not on the number of peaks.

  • Content Type Journal Article
  • Category Original Paper
  • Pages 1-16
  • DOI 10.1007/s00500-011-0781-5
  • Authors
    • Jesús Marín, Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya, EUETIB, Urgell 187, 08036 Barcelona, Spain
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Towards interval-based non-additive deconvolution in signal processing

Posted on October 15, 2011 by Community

Abstract  
Reconstructing a signal from its observations via a sensor device is usually called “deconvolution”. Such reconstruction requires
perfect knowledge of the impulse response of the sensor involved in the signal measurement. The lower this knowledge, the
more biased the reconstruction. In this paper, we present a novel method for reconstructing a signal measured by a sensor
whose impulse response is imprecisely known. This technique is based on modeling the relationship between the measurement
and the signal via a concave capacity and extending the convolution concept to a concave set of impulse responses. The reconstructed
signal is interval-valued, thus reflecting the poor knowledge of the sensor impulse response.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-12
  • DOI 10.1007/s00500-011-0771-7
  • Authors
    • Olivier Strauss, LIRMM Université Montpellier II, 161 rue Ada, 34392 Montpellier cedex 5, France
    • Agnès Rico, LIRMM Université Montpellier II, 161 rue Ada, 34392 Montpellier cedex 5, France
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Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis

Posted on October 15, 2011 by Community

Abstract  
Using a statistical model in a diagnosis task generally requires a large amount of labeled data. When ground truth information
is not available, too expensive or difficult to collect, one has to rely on expert knowledge. In this paper, it is proposed
to use partial information from domain experts expressed as belief functions. Expert opinions are combined in this framework
and used with measurement data to estimate the parameters of a statistical model using a variant of the EM algorithm. The
particular application investigated here concerns the diagnosis of railway track circuits. A noiseless Independent Factor
Analysis model is postulated, assuming the observed variables extracted from railway track inspection signals to be generated
by a linear mixture of independent latent variables linked to the system component states. Usually, learning with this statistical
model is performed in an unsupervised way using unlabeled examples only. In this paper, it is proposed to handle this learning
process in a soft-supervised way using imperfect information on the system component states. Fusing partially reliable information
about cluster membership is shown to significantly improve classification results.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-14
  • DOI 10.1007/s00500-011-0766-4
  • Authors
    • Zohra L. Cherfi, GRETTIA, French Institute of Science and Technology for Transport, Development and Networks, Université Paris-Est, Marne-la-Vallée, France
    • Latifa Oukhellou, LISSI, Université Paris-Est Créteil, Créteil, France
    • Etienne Côme, GRETTIA, French Institute of Science and Technology for Transport, Development and Networks, Université Paris-Est, Marne-la-Vallée, France
    • Thierry Denœux, HEUDIASYC, Université de Technologie de Compiègne, UMR CNRS 6599, Compiègne, France
    • Patrice Aknin, GRETTIA, French Institute of Science and Technology for Transport, Development and Networks, Université Paris-Est, Marne-la-Vallée, France
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Missing data imputation for fuzzy rule-based classification systems

Posted on October 13, 2011 by Community

Abstract  
Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain
good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life
data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several
schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well
known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches
to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which
we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for
FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined
missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation
methods conditioned to the type of FRBCSs is required.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-19
  • DOI 10.1007/s00500-011-0774-4
  • Authors
    • Julián Luengo, Deptartment of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
    • José A. Sáez, Deptartment of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
    • Francisco Herrera, Deptartment of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
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Extending information processing in a Fuzzy Random Forest ensemble

Posted on October 13, 2011 by Community

Abstract  
Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate
imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty, and imprecision
that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble for classification and show its ability
to handle imperfect data into the learning and the classification phases. Then, we will describe the types of imperfect data
it supports. We will devise an augmented ensemble that can operate with others type of imperfect data: crisp, missing, probabilistic
uncertainty, and imprecise (fuzzy and crisp) values. Additionally, we will perform experiments with imperfect datasets created
for this purpose and datasets used in other papers to show the advantage of being able to express the true nature of imperfect
information.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-17
  • DOI 10.1007/s00500-011-0777-1
  • Authors
    • Jose M. Cadenas, Dept. Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • M. Carmen Garrido, Dept. Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • Raquel Martínez, Dept. Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
    • Piero P. Bonissone, GE Global Research, One Research Circle, Niskayuna, NY 12309, USA
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Mining fuzzy association rules from low-quality data

Posted on October 13, 2011 by Community

Abstract  
Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily
analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining
association rules from databases with crisp values. However, the data in many real-world applications have a certain degree
of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting
knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori
mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early
childhood were made to verify the performance of the proposed algorithm.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-19
  • DOI 10.1007/s00500-011-0775-3
  • Authors
    • A. M. Palacios, Department of Computer Science, University of Oviedo, 33204 Gijón, Spain
    • M. J. Gacto, Department of Computer Science, University of Jaén, 23071 Jaén, Spain
    • J. Alcalá-Fdez, Department of Computer Science and Artificial Intelligence, CITIC-UGR, University of Granada, 18071 Granada, Spain
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