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

Entropy-type classification maximum likelihood algorithms for mixture models

Abstract  Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood
(CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. exten…

Abstract  

Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood
(CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML.
Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper,
we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type
CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method
provides better results than some existing methods.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0560-8
  • Authors
    • Chien-Yo Lai, Chung Yuan Christian University Department of Applied Mathematics Chung-Li 32023 Taiwan
    • Miin-Shen Yang, Chung Yuan Christian University Department of Applied Mathematics Chung-Li 32023 Taiwan

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

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

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

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue
Content Type Journal ArticlePages 1-2DOI 10.1007/s00500-010-0559-1Authors
Haiying Wang, University of Ulster School of Computing and Mathematics and…

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue

  • Content Type Journal Article
  • Pages 1-2
  • DOI 10.1007/s00500-010-0559-1
  • Authors
    • Haiying Wang, University of Ulster School of Computing and Mathematics and Computer Science Research Institute Shore Road Newtownabbey Co. Antrim BT37 0QB UK
    • Yixin Chen, Washington University in St Louis Computer Science and Engineering Campus Box 1045, One Brookings Drive St. Louis MO 63130 USA
    • Hepu Deng, RMIT University Business Information Technology GPO Box 2476 Melbourne VIC 3001 Australia
    • Lipo Wang, Nanyang Technological University School of Electrical and Electronic Engineering Block S1, 50 Nanyang Avenue Singapore 639798 Singapore

A novel approach to annotating web service based on interface concept mapping and semantic expansion

Abstract  With the rapid development of web service technology in these years, traditional standards have been matured during the process
of service registry and discovery. However, it is difficult for service requesters to discover satisfac…

Abstract  

With the rapid development of web service technology in these years, traditional standards have been matured during the process
of service registry and discovery. However, it is difficult for service requesters to discover satisfactory web services.
The reason for this phenomenon is that the traditional service organization mode lacks semantic understanding ability for
service function interface. This paper proposes a novel approach to annotating web services. We first adopt domain ontology
as a semantic context, and give our general framework of service semantic annotation. Then, interface concept mapping algorithm
and service interface expansion algorithm are respectively presented in detail. Finally, the generation process of semantic
web service repository is presented based on preceding algorithms. Simulation experiment results demonstrate that annotated
web services by the proposed method can more satisfy requirements for service requesters than traditional ones by service
matchmaking engine. It can get better service discovery effectiveness.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0548-4
  • Authors
    • Guobing Zou, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yang Xiang, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yanglan Gan, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yixin Chen, Washington University Department of Computer Science and Engineering St. Louis MO 63130 USA

A novel multi-population cultural algorithm adopting knowledge migration

Abstract  In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However,
migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. …

Abstract  

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However,
migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance
the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge
extracted from the evolution process of each sub-population directly reflects the information about dominant search space.
By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction
with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that
the algorithm can effectively improve the speed of convergence and overcome premature convergence.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0556-4
  • Authors
    • Yi-nan Guo, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Jian Cheng, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Yuan-yuan Cao, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Yong Lin, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China

Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations

Abstract  This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current
EDAs require much time in the statistical learning process as the relationships among the variables are t…

Abstract  This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current
EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the
selfish gene theory (SG) is deployed in this approach and a mutual information and entropy based cluster (MIEC) model with
an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population.
Experimental results on several benchmark problems demonstrate that, compared with BMDA, COMIT and MIMIC, SGMIEC often performs
better in convergent reliability, convergent velocity and convergent process.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0557-3
  • Authors
    • Feng Wang, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Zhiyi Lin, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Cheng Yang, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Yuanxiang Li, Wuhan University State Key Laboratory of Software Engineering Wuhan China

Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments

Abstract  In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented
by a probability model. This means that the priority search areas of the solution space are characterize…

Abstract  In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented
by a probability model. This means that the priority search areas of the solution space are characterized by the probability
model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt
binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize
the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A
diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the
static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the
EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal
distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand
the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.

  • Content Type Journal Article
  • Pages 1-16
  • DOI 10.1007/s00500-010-0547-5
  • Authors
    • Xingguang Peng, Northwestern Polytechnical University School of Electronics and Information Xi’an Shaanxi 710129 China
    • Xiaoguang Gao, Northwestern Polytechnical University School of Electronics and Information Xi’an Shaanxi 710129 China
    • Shengxiang Yang, University of Leicester Department of Computer Science University Road Leicester LE1 7RH UK

Solution of fuzzy polynomial equations by modified Adomian decomposition method

Abstract  In this paper, we present some efficient numerical algorithm for solving fuzzy polynomial equations based on Newton’s method.
The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numeri…

Abstract  In this paper, we present some efficient numerical algorithm for solving fuzzy polynomial equations based on Newton’s method.
The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numerical illustrations are
given to show the efficiency of algorithms.

  • Content Type Journal Article
  • Pages 1-6
  • DOI 10.1007/s00500-010-0546-6
  • Authors
    • M. Otadi, Islamic Azad University Department of Mathematics Firuozkooh Branch Firuozkooh Iran
    • M. Mosleh, Islamic Azad University Department of Mathematics Firuozkooh Branch Firuozkooh Iran