Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

Abstract  This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns
first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm…

Abstract  

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns
first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer
system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some
improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve
better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for
classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with
both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative
algorithms in this field.

  • Content Type Journal Article
  • Pages 1-15
  • DOI 10.1007/s00500-010-0678-8
  • Authors
    • L. Ignacio Lopez, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Juan M. Bardallo, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Miguel A. De Vega, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Antonio Peregrin, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain

Fuzzy knowledge representation study for incremental learning in data streams and classification problems

Abstract  The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which
data are made available online. This has led to the design of several systems that create data models on…

Abstract  

The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which
data are made available online. This has led to the design of several systems that create data models online. A novel approach
to online learning of data streams can be found in Fuzzy-UCS, a young Michigan-style fuzzy-classifier system that has recently
demonstrated to be highly competitive in extracting classification models from complex domains. Despite the promising results
reported for Fuzzy-UCS, there still remain some hot issues that need to be analyzed in detail. This paper carefully studies
two key aspects in Fuzzy-UCS: the ability of the system to learn models from data streams where concepts change over time
and the behavior of different fuzzy representations. Four fuzzy representations that move through the dimensions of flexibility
and interpretability are included in the system. The behavior of the different representations on a problem with concept changes
is studied and compared to other machine learning techniques prepared to deal with these types of problems. Thereafter, the
comparison is extended to a large collection of real-world problems, and a close examination of which problem characteristics
benefit or affect the different representations is conducted. The overall results show that Fuzzy-UCS can effectively deal
with problems with concept changes and lead to different interesting conclusions on the particular behavior of each representation.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-26
  • DOI 10.1007/s00500-010-0668-x
  • Authors
    • Albert Orriols-Puig, Grup de Recerca en Sistemes Intel·ligents, La Salle-Universitat Ramon Llull, 08022 Barcelona, Spain
    • Jorge Casillas, Department of Computer Science and Artificial Intelligence, Research Center on Communication and Information Technology (CITIC-UGR), University of Granada, 18071 Granada, Spain

RM approach for ranking of L–R type generalized fuzzy numbers

Abstract  Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked
before an action is taken by a decision maker. In this paper, with the help of several counter examples…

Abstract  

Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked
before an action is taken by a decision maker. In this paper, with the help of several counter examples it is proved that
ranking method proposed by Chen and Chen (Expert Syst Appl 36:6833–6842, 2009) is incorrect. The main aim of this paper is to propose a new approach for the ranking of L–R type generalized fuzzy numbers. The proposed ranking approach is based on rank and mode so it is named as RM approach. The
main advantage of the proposed approach is that it provides the correct ordering of generalized and normal fuzzy numbers
and it is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfies all
the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Fuzzy Sets Syst 118:375–385, 2001).

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0676-x
  • Authors
    • Amit Kumar, School of Mathematics and Computer Applications, Thapar University, Patiala, 147 004 India
    • Pushpinder Singh, School of Mathematics and Computer Applications, Thapar University, Patiala, 147 004 India
    • Parmpreet Kaur, School of Mathematics and Computer Applications, Thapar University, Patiala, 147 004 India
    • Amarpreet Kaur, School of Mathematics and Computer Applications, Thapar University, Patiala, 147 004 India

An evolutionary approach to enhance data privacy

Abstract  Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative
masking methods propose the distortion of the original data sets before publication, tackling a diffic…

Abstract  

Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative
masking methods propose the distortion of the original data sets before publication, tackling a difficult tradeoff between
data utility (low information loss) and protection against disclosure (low disclosure risk). In this paper, we describe how
information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced
masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art
protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets
in order to illustrate and empirically evaluate the application of this technique.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0672-1
  • Authors
    • Javier Jiménez, IIIA, Artificial Intelligence Research Institute, CSIC, Consejo Superior de Investigaciones Cientficas, Campus UAB, 08193 Bellaterra, Catalonia Spain
    • Jordi Marés, IIIA, Artificial Intelligence Research Institute, CSIC, Consejo Superior de Investigaciones Cientficas, Campus UAB, 08193 Bellaterra, Catalonia Spain
    • Vicenç Torra, IIIA, Artificial Intelligence Research Institute, CSIC, Consejo Superior de Investigaciones Cientficas, Campus UAB, 08193 Bellaterra, Catalonia Spain

Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions

Abstract  Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using
a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute…

Abstract  

Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using
a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute. Then, a multiobjective
evolutionary algorithm is applied to perform fuzzy rule selection. Since using multiple granularities for the same attribute
has been sometimes pointed out as to involve a potential interpretability loss, a mechanism to specify appropriate single
granularities at the rule extraction stage has been proposed to avoid it but maintaining or even improving the classification
performance. In this work, we perform a statistical study on this proposal and we extend it by combining the single granularity-based
approach with a lateral tuning of the membership functions, i.e., complete contexts learning. In this way, we analyze in depth
the importance of determining the appropriate contexts for learning fuzzy classifiers. To this end, we will compare the single
granularity-based approach with the use of multiple granularities with and without tuning. The results show that the performance
of the obtained classifiers can be even improved by obtaining the appropriate variable contexts, i.e., appropriate granularities
and membership function parameters.

  • Content Type Journal Article
  • Pages 1-16
  • DOI 10.1007/s00500-010-0671-2
  • Authors
    • Rafael Alcalá, Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
    • Yusuke Nojima, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan
    • Francisco Herrera, Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
    • Hisao Ishibuchi, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

Abstract  This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study
on the type of patients who tend to visit the psychiatric emergency department in a given period of time of t…

Abstract  

This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study
on the type of patients who tend to visit the psychiatric emergency department in a given period of time of the day. In this
problem, the objective is to characterise subgroups of patients according to their time of arrival at the emergency department.
To solve this problem, several subgroup discovery algorithms have been applied to determine which of them obtains better results.
The multiobjective evolutionary algorithm MESDIF for the extraction of fuzzy rules obtains better results and so it has been
used to extract interesting information regarding the rate of admission to the psychiatric emergency department.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-14
  • DOI 10.1007/s00500-010-0670-3
  • Authors
    • C. J. Carmona, Department of Computer Science, University of Jaen, 23071 Jaen, Spain
    • P. González, Department of Computer Science, University of Jaen, 23071 Jaen, Spain
    • M. J. del Jesus, Department of Computer Science, University of Jaen, 23071 Jaen, Spain
    • M. Navío-Acosta, Hospital Universitario 12 de Octubre, CIBERSAM, Madrid, Spain
    • L. Jiménez-Trevino, Department of Psychiatry, University of Oviedo, CIBERSAM, Oviedo, Spain

Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering

Abstract  Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific
purposes. Most of the previous approaches set a single minimum support threshold for all the items and identi…

Abstract  

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific
purposes. Most of the previous approaches set a single minimum support threshold for all the items and identify the relationships
among transactions using binary values. In real applications, different items may have different criteria to judge their importance.
In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and
fuzzy association rules from quantitative transactions. It used requirement satisfaction and suitability of membership functions
to evaluate fitness values of chromosomes. The calculation for requirement satisfaction might take a lot of time, especially
when the database to be scanned could not be totally fed into main memory. In this paper, an enhanced approach, called the
fuzzy cluster-based genetic-fuzzy mining approach for items with multiple minimum supports (FCGFMMS), is thus proposed to
speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes
in a population into several clusters by the fuzzy k-means clustering approach and evaluates each individual according to both their cluster and their own information. Experimental
results also show the effectiveness and the efficiency of the proposed approach.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-15
  • DOI 10.1007/s00500-010-0664-1
  • Authors
    • Chun-Hao Chen, Department of Computer Science and Information Engineering, Tamkang University, Taipei, 251 Taiwan, ROC
    • Tzung-Pei Hong, Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811 Taiwan, ROC
    • Vincent S. Tseng, Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 701 Taiwan, ROC

Special issue on evolutionary fuzzy systems

Special issue on evolutionary fuzzy systems
Content Type Journal ArticleCategory FocusPages 1-3DOI 10.1007/s00500-010-0663-2Authors
Yusuke Nojima, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka 599-…

Special issue on evolutionary fuzzy systems

  • Content Type Journal Article
  • Category Focus
  • Pages 1-3
  • DOI 10.1007/s00500-010-0663-2
  • Authors
    • Yusuke Nojima, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan
    • Rafael Alcalá, Department of Computer Science and A.I., University of Granada, E-18071 Granada, Spain
    • Hisao Ishibuchi, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan
    • Francisco Herrera, Department of Computer Science and A.I., University of Granada, E-18071 Granada, Spain

Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

Abstract  Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable
fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy s…

Abstract  

Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable
fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO
algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability.
In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy
rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants,
and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine
learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated
for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions.
Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further
improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-20
  • DOI 10.1007/s00500-010-0669-9
  • Authors
    • Hisao Ishibuchi, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, 599-8531 Japan
    • Yusuke Nakashima, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, 599-8531 Japan
    • Yusuke Nojima, Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, 599-8531 Japan

A multistage genetic fuzzy classifier for land cover classification from satellite imagery

Abstract  A linguistic boosted genetic fuzzy classifier (LiBGFC) is proposed in this paper for land cover classification from multispectral
images. The LiBGFC is a three-stage process, aiming at effectively tackling the interpretability vers…

Abstract  

A linguistic boosted genetic fuzzy classifier (LiBGFC) is proposed in this paper for land cover classification from multispectral
images. The LiBGFC is a three-stage process, aiming at effectively tackling the interpretability versus accuracy tradeoff
problem. The first stage iteratively generates fuzzy rules, as directed by a boosting algorithm that localizes new rules in
uncovered subspaces of the feature space, implicitly preserving the cooperation with previously derived ones. Each rule is
able to select the required features, further improving the interpretability of the obtained model. Special provision is taken
in the formulation of the fitness function to avoid the creation of redundant rules. A simplification stage follows the first
one aiming at further improving the interpretability of the initial rule base, providing a more compact and interpretable
solution. Finally, a genetic tuning stage fine tunes the fuzzy sets database improving the classification performance of the
obtained model. The LiBGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international
importance. The results indicate the effectiveness of the proposed system in handling multidimensional feature spaces, producing
easily understandable fuzzy models.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-20
  • DOI 10.1007/s00500-010-0666-z
  • Authors
    • D. G. Stavrakoudis, Division of Electronics and Computer Engineering, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    • J. B. Theocharis, Division of Electronics and Computer Engineering, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    • G. C. Zalidis, Laboratory of Applied Soil Science, Faculty of Agronomy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece