A cost-efficient and versatile sanitizing algorithm by using a greedy approach

Abstract  In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confidentiality
protection of the information has been a long-term goal pursued by the database security researc…

Abstract  

In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confidentiality
protection of the information has been a long-term goal pursued by the database security research community and the government
statistical agencies. In this paper, we proposed greedy methods for hiding sensitive rules. The experimental results showed
the effectiveness of our approaches in terms of undesired side effects avoided in the rule hiding process. The results also
revealed that in most cases, all the sensitive rules are hidden without generating spurious rules. First, the good scalability
of our approach in terms of database sizes was achieved by using an efficient data structure, FCET, to store only maximal
frequent itemsets instead of storing all frequent itemsets. Furthermore, we also proposed a new framework for enforcing the
privacy in mining association rules. In the framework, we combined the techniques of efficiently hiding sensitive rules with
the transaction retrieval engine based on the FCET index tree. For hiding sensitive rules, the proposed greedy approach includes
a greedy approximation algorithm and a greedy exhausted algorithm to sanitize the database. In particular, we presented four
strategies in the sanitizing procedure and four strategies in the exposed procedure, respectively, for hiding a group of association
rules characterized as sensitive or artificial rules. In addition, the exposed procedure would expose missing rules during
the processing so that the number of missing rules could be lowered as much as possible.

  • Content Type Journal Article
  • Pages 1-14
  • DOI 10.1007/s00500-010-0549-3
  • Authors
    • Chieh-Ming Wu, National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology 123 University Road, Section 3, Touliu Yunlin 640 Taiwan, ROC
    • Yin-Fu Huang, National Yunlin University of Science and Technology Department of Computer Science and Information Engineering 123 University Road, Section 3, Touliu Yunlin 640 Taiwan, ROC

SIGEVOlution Volume 4, Issue 3, is now available

New issues of the SIGEVOlution newsletter are appearing quite rapidly and the latest—Volume 4 Issue 3—is now available for you to download from: http://www.sigevolution.orgThe new issue features:Issues in Applying Computational Intelligence by Arth…

New issues of the SIGEVOlution newsletter are appearing quite rapidly and the latest—Volume 4 Issue 3—is now available for you to download from: http://www.sigevolution.org
The new issue features:
  • Issues in Applying Computational Intelligence by Arthur Kordon
  • JavaXCSF by Patrick O. Stalph & Martin V. Butz
  • Dissertation corner
  • New issues of journals
  • Calls & calendar
The newsletter is intended to be viewed electronically.
Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

GPEM 11(1) hardcopy — new color!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Abstract  We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console
by using a publicly available common framework for the devices called XNA (for “XNA’s Not Acronymed

Abstract  

We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console
by using a publicly available common framework for the devices called XNA (for “XNA’s Not Acronymed”). By constructing the
LGP within this framework, we effectively produce an LGP “game” for PC and XBox 360 that displays results as they evolve.
We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing
a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations
on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around
a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement
the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still
maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation
of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following
the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described.
Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the
metrics of GP operations per second, actual time elapsed, speedup of parallel over sequential implementation, and percentage
of execution time used by the GPU versus CPU.

  • Content Type Journal Article
  • Pages 147-184
  • DOI 10.1007/s10710-010-9102-5
  • Authors
    • Garnett Wilson, Memorial University of Newfoundland Department of Computer Science St. John’s NL A1B 3X5 Canada
    • Wolfgang Banzhaf, Memorial University of Newfoundland Department of Computer Science St. John’s NL A1B 3X5 Canada

Stabilization of nonlinear time-delay systems with input saturation via anti-windup fuzzy design

Abstract  This investigation considers stability analysis and control design for nonlinear time-delay systems subject to input saturation.
An anti-windup fuzzy control approach, based on fuzzy modeling of nonlinear systems, is developed to d…

Abstract  

This investigation considers stability analysis and control design for nonlinear time-delay systems subject to input saturation.
An anti-windup fuzzy control approach, based on fuzzy modeling of nonlinear systems, is developed to deal with the problems
of stabilization of the closed-loop system and enlargement of the domain of attraction. To facilitate the designing work,
the nonlinearity of saturation is first characterized by sector conditions, which provide a basis for analysis and synthesis
of the anti-windup fuzzy control scheme. Then, the Lyapunov–Krasovskii delay-independent and delay-dependent functional approaches
are applied to establish sufficient conditions that ensure convergence of all admissible initial states within the domain
of attraction. These conditions are formulated as a convex optimization problem with constraints provided by a set of linear
matrix inequalities. Finally, numeric examples are given to validate the proposed method.

  • Content Type Journal Article
  • Pages 1-12
  • DOI 10.1007/s00500-010-0555-5
  • Authors
    • Chen-Sheng Ting, National Formosa University Department of Electrical Engineering Yunlin 632 Taiwan, ROC
    • Chuan-Sheng Liu, National Formosa University Department of Aeronautical Engineering Yunlin 632 Taiwan, ROC

SIGEVOlution Volume 4 Issue 3

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org
The issue features:

Issues in Applying Computational Intelligence by Arthur Kordon
JavaXCSF by Patrick O. Stalph & Martin V. Butz
Dissertation Corner
New issues of journals
Calls & calendar

The newsletter is intended to be viewed electronically.
Pier Luca Lanzi (EIC)

Portland, Oregon

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org

The issue features:

  • Issues in Applying Computational Intelligence by Arthur Kordon
  • JavaXCSF by Patrick O. Stalph & Martin V. Butz
  • Dissertation Corner
  • New issues of journals
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

SIGEVOlution Volume 4 Issue 3

The new issue of SIGEVOlution is now available for you to download from: http://www.sigevolution.org The issue features: Issues in Applying Computational Intelligence by Arthur Kordon JavaXCSF by Patrick O. Stalph & Martin V. Butz Dissertation Corner New issues of journals … Continue reading

Portland, Oregon

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org

The issue features:

  • Issues in Applying Computational Intelligence by Arthur Kordon
  • JavaXCSF by Patrick O. Stalph & Martin V. Butz
  • Dissertation Corner
  • New issues of journals
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

SIGEVOlution Volume 4 Issue 3

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org
The issue features:

Issues in Applying Computational Intelligence by Arthur Kordon
JavaXCSF by Patrick O. Stalph & Martin V. Butz
Dissertation Corner
New issues of journals
Calls & calendar

The newsletter is intended to be viewed electronically.
Pier Luca Lanzi (EIC)
Related Posts

Portland, Oregon

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org

The issue features:

  • Issues in Applying Computational Intelligence by Arthur Kordon
  • JavaXCSF by Patrick O. Stalph & Martin V. Butz
  • Dissertation Corner
  • New issues of journals
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

Electrostatic field framework for supervised and semi-supervised learning from incomplete data

Abstract  In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
approach to exploiting incomplete training data with missing features, involving extensive use of electrostat…

Abstract  

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy,
has been used. The framework supports a hybrid supervised and unsupervised training scenario, enabling learning simultaneously
from both labelled and unlabelled data using the same set of rules and adaptation mechanisms. Classification of incomplete
patterns has been facilitated by introducing a local dimensionality reduction technique, which aims at exploiting all available
information using the data ‘as is’, rather than trying to estimate the missing values. The performance of all proposed methods
has been extensively tested in a wide range of missing data scenarios, using a number of standard benchmark datasets in order
to make the results comparable with those available in current and future literature. Several modifications to the original
Electrostatic Field Classifier aiming at improving speed and robustness in higher dimensional spaces have also been introduced
and discussed.

  • Content Type Journal Article
  • Pages 921-945
  • DOI 10.1007/s11047-010-9182-4
  • Authors
    • Marcin Budka, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK
    • Bogdan Gabrys, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

Abstract  This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary
support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs …

Abstract  

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary
support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of

éN/3ù+1

. A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities
are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier.
The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from
a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with
the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper
bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to
be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies
show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared
to the popular 1-vs-1 alternative.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0551-9
  • Authors
    • Xiao-Lei Xia, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK
    • Kang Li, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK
    • George W. Irwin, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK