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

Learning and backtracking in non-preemptive scheduling of tasks under timing constraints

Abstract  We propose two novel heuristic search techniques to address the problem of scheduling tasks under hard timing constraints
on a single processor architecture. The underlying problem is NP-hard in the strong sense and it is a fundame…

Abstract  

We propose two novel heuristic search techniques to address the problem of scheduling tasks under hard timing constraints
on a single processor architecture. The underlying problem is NP-hard in the strong sense and it is a fundamental challenge
in feedback-control theory and automated cybernetics. The proposed techniques are a learning-based approaches and they take
much less memory space. A partial feasible schedule is maintained and extended over a repeated problem solving trials, previously
assigned priorities are refined according to the gained information about the problem to lead the convergence to a complete
feasible schedule if one exists. First, we present the learning in hard-real-time with single learning (LHRTS-SL) algorithm
where a single learning function is utilized, then we discuss its drawback and we propose the LHRTS with double learning algorithm
in which a second learning function is integrated to cope up with LHRTS-SL drawback. Experimental results show the efficiency
of the proposed techniques in terms of success ratio when used to schedule randomly generated problem instances.

  • Content Type Journal Article
  • Pages 1-16
  • DOI 10.1007/s00500-010-0582-2
  • Authors
    • Yacine Laalaoui, National Computer Science School 16309 Oued-Smar Algiers Algeria
    • Habiba Drias, National Computer Science School 16309 Oued-Smar Algiers Algeria

CHSMST: a clustering algorithm based on hyper surface and minimum spanning tree

Abstract  As data mining having attracted a significant amount of research attention, many clustering algorithms have been proposed
in the past decades. However, most of existing clustering methods have high computational time or are not sui…

Abstract  

As data mining having attracted a significant amount of research attention, many clustering algorithms have been proposed
in the past decades. However, most of existing clustering methods have high computational time or are not suitable for discovering
clusters with non-convex shape. In this paper, an efficient clustering algorithm CHSMST is proposed, which is based on clustering
based on hyper surface (CHS) and minimum spanning tree. In the first step, CHSMST applies CHS to obtain initial clusters immediately.
Thereafter, minimum spanning tree is introduced to handle locally dense data which is hard for CHS to deal with. The experiments
show that CHSMST can discover clusters with arbitrary shape. Moreover, CHSMST is insensitive to the order of input samples
and the run time of the algorithm increases moderately as the scale of dataset becomes large.

  • Content Type Journal Article
  • Pages 1-7
  • DOI 10.1007/s00500-010-0585-z
  • Authors
    • Qing He, Chinese Academy of Sciences The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology 100190 Beijing China
    • Weizhong Zhao, Chinese Academy of Sciences The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology 100190 Beijing China
    • Zhongzhi Shi, Chinese Academy of Sciences The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology 100190 Beijing China

High speed detection of retinal blood vessels in fundus image using phase congruency

Abstract  Detection of blood vessels in retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist
several methods to automatically detect blood vessels from retinal image with the aid of different computa…

Abstract  

Detection of blood vessels in retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist
several methods to automatically detect blood vessels from retinal image with the aid of different computational methods.
However, all these methods require lengthy processing time. The method proposed here acquires binary vessels from a RGB retinal
fundus image in almost real time. Initially, the phase congruency of a retinal image is generated, which is a soft-classification
of blood vessels. Phase congruency is a dimensionless quantity that is invariant to changes in image brightness or contrast;
hence, it provides an absolute measure of the significance of feature points. This experiment acquires phase congruency of
an image using Log-Gabor wavelets. To acquire a binary segmentation, thresholds are applied on the phase congruency image.
The process of determining the best threshold value is based on area under the relative operating characteristic (ROC) curve.
The proposed method is able to detect blood vessels in a retinal fundus image within 10 s on a PC with (accuracy, area under
ROC curve) = (0.91, 0.92), and (0.92, 0.94) for the STARE and the DRIVE databases, respectively.

  • Content Type Journal Article
  • Pages 1-14
  • DOI 10.1007/s00500-010-0574-2
  • Authors
    • M. Ashraful Amin, Independent University Bangladesh School of Engineering and Computer Science Dhaka Bangladesh
    • Hong Yan, City University of Hong Kong Department of Electronic Engineering Kowloon Hong Kong

Commutative bounded integral residuated orthomodular lattices are Boolean algebras

Abstract  We show that a commutative bounded integral orthomodular lattice is residuated iff it is a Boolean algebra. This result is
a consequence of (Ward, Dilworth in Trans Am Math Soc 45, 336–354, 1939, Theorem 7.31); however, out …

Abstract  

We show that a commutative bounded integral orthomodular lattice is residuated iff it is a Boolean algebra. This result is
a consequence of (Ward, Dilworth in Trans Am Math Soc 45, 336–354, 1939, Theorem 7.31); however, out proof is independent and uses other instruments.

  • Content Type Journal Article
  • Pages 1-2
  • DOI 10.1007/s00500-010-0572-4
  • Authors
    • Josef Tkadlec, Czech Technical University 166 27 Praha Czech Republic
    • Esko Turunen, Tampere University of Technology P.O. Box 553 33101 Tampere Finland

Mining potentially more interesting association rules with fuzzy interest measure

Abstract  The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of
potentially useful information hidden in databases. However, only considering the constraints of minimum …

Abstract  

The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of
potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum
confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an
association rule may be. It is indicated by the membership values belonging to two fuzzy sets (i.e., the stronger rule set
and the weaker rule set), and thus provides much more flexibility than traditional methods to discover some potentially more
interesting association rules. Furthermore, revised algorithms based on Apriori algorithm and matrix structure are designed
under this framework.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0579-x
  • Authors
    • Wei-Min Ma, School of Economics and Management, Tongji University, Shanghai, 200092 China
    • Ke Wang, School of Economics and Management, Tongji University, Shanghai, 200092 China
    • Zhu-Ping Liu, School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing, 100083 China

Recent advances on machine learning and Cybernetics

Recent advances on machine learning and Cybernetics
Content Type Journal ArticlePages 1-1DOI 10.1007/s00500-010-0590-2Authors
Witold Pedrycz, University of Alberta Edmonton CanadaDaniel Yeung, Machine Learning Institute Kowloon Hong KongXizhao Wang,…

Recent advances on machine learning and Cybernetics

  • Content Type Journal Article
  • Pages 1-1
  • DOI 10.1007/s00500-010-0590-2
  • Authors
    • Witold Pedrycz, University of Alberta Edmonton Canada
    • Daniel Yeung, Machine Learning Institute Kowloon Hong Kong
    • Xizhao Wang, Hebei University Hebei China

Automatic localization and annotation of facial features using machine learning techniques

Abstract  Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using
low level features, such as colour histograms. However, this requires a user to provide an image to the …

Abstract  

Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using
low level features, such as colour histograms. However, this requires a user to provide an image to the system. It is easier
for a user to query the CBIR system using search terms which requires the image content to be described by semantic labels.
However, finding a relationship between the image features and semantic labels is a challenging problem to solve. This paper
aims to discover semantic labels for facial features for use in a face image retrieval system. Face image retrieval traditionally
uses global face-image information to determine similarity between images. However little has been done in the field of face
image retrieval to use local face-features and semantic labelling. Our work aims to develop a clustering method for the discovery
of semantic labels of face-features. We also present a machine learning based face-feature localization mechanism which we
show has promise in providing accurate localization.

  • Content Type Journal Article
  • Pages 1-15
  • DOI 10.1007/s00500-010-0586-y
  • Authors
    • Paul C. Conilione, La Trobe University Department of Computer Science and Computer Engineering Melbourne VIC 3086 Australia
    • Dianhui Wang, La Trobe University Department of Computer Science and Computer Engineering Melbourne VIC 3086 Australia

Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets

Abstract  A fuzzy support vector machine (FSVM) is an improvement in SVMs for dealing with data sets with outliers. In FSVM, a key step
is to compute the membership for every training sample. Existing approaches of computing the membership o…

Abstract  

A fuzzy support vector machine (FSVM) is an improvement in SVMs for dealing with data sets with outliers. In FSVM, a key step
is to compute the membership for every training sample. Existing approaches of computing the membership of a sample are motivated
by the existence of outliers in data sets and do not take account of the inconsistency between conditional attributes and
decision classes. However, this kind of inconsistency can affect membership for every sample and has been considered in fuzzy
rough set theory. In this paper, we develop a new method to compute membership for FSVMs by using a Gaussian kernel-based
fuzzy rough set. Furthermore, we employ a technique of attribute reduction using Gaussian kernel-based fuzzy rough sets to
perform feature selection for FSVMs. Based on these discussions we combine the FSVMs and fuzzy rough sets methods together.
The experimental results show that the proposed approaches are feasible and effective.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0577-z
  • Authors
    • Qiang He, Harbin Institute of Technology Department of Mathematics Harbin 150001 People’s Republic of China
    • Congxin Wu, Harbin Institute of Technology Department of Mathematics Harbin 150001 People’s Republic of China

Fuzzy decision tree based on fuzzy-rough technique

Abstract  Using an efficient criterion in selection of fuzzy conditional attributes (i.e. expanded attributes) is important for generation
of fuzzy decision trees. Given a fuzzy information system (FIS), fuzzy conditional attributes play a c…

Abstract  

Using an efficient criterion in selection of fuzzy conditional attributes (i.e. expanded attributes) is important for generation
of fuzzy decision trees. Given a fuzzy information system (FIS), fuzzy conditional attributes play a crucial role in fuzzy
decision making. Besides, different fuzzy conditional attributes have different influences on decision making, and some of
them may be more important than the others. Two well-known criteria employed to select expanded attributes are fuzzy classification
entropy and classification ambiguity, both of which essentially use the ratio of uncertainty to measure the significance of
fuzzy conditional attributes. Based on fuzzy-rough technique, this paper proposes a new criterion, in which expanded attributes
are selected by using significance of fuzzy conditional attributes with respect to fuzzy decision attributes. An illustrative
example as well as the experimental results demonstrates the effectiveness of our proposed method.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0584-0
  • Authors
    • Jun-hai Zhai, Hebei University Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science Baoding 071002 Hebei China

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

Abstract  Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in
many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses …

Abstract  

Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in
many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based
migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO,
for the global numerical optimization problem. DE/BBO combines the exploration of DE with the exploitation of BBO effectively,
and hence it can generate the promising candidate solutions. To verify the performance of our proposed DE/BBO, 23 benchmark
functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach
is effective and efficient. Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably,
in terms of the quality of the final solutions and the convergence rate. In addition, the influence of the population size,
dimensionality, different mutation schemes, and the self-adaptive control parameters of DE are also studied.

  • Content Type Journal Article
  • Pages 1-21
  • DOI 10.1007/s00500-010-0591-1
  • Authors
    • Wenyin Gong, China University of Geosciences School of Computer Science Wuhan People’s Republic of China
    • Zhihua Cai, China University of Geosciences School of Computer Science Wuhan People’s Republic of China
    • Charles X. Ling, The University of Western Ontario Department of Computer Science London Canada