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Volume 13 (2023): Issue 2 (March 2023)

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Volume 7 (2017): Issue 2 (April 2017)

Volume 7 (2017): Issue 1 (January 2017)

Volume 6 (2016): Issue 4 (October 2016)

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Volume 6 (2016): Issue 2 (April 2016)

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Volume 5 (2015): Issue 4 (October 2015)

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Volume 3 (2013): Issue 4 (October 2013)

Volume 3 (2013): Issue 3 (July 2013)

Volume 3 (2013): Issue 2 (April 2013)

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Journal Details
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Search

Volume 7 (2017): Issue 2 (April 2017)

Journal Details
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Search

5 Articles
Open Access

Swarm Intelligence Algorithm Based on Competitive Predators with Dynamic Virtual Teams

Published Online: 23 Feb 2017
Page range: 87 - 101

Abstract

Abstract

In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature convergence for multimodal problems. In FPO, all of the particles are seen as predators. Only the competitive, powerful predator that are selected as an elite could achieve the limited opportunity to update. The elite generation with roulette wheel selection could increase individual independence and reduce rapid social collaboration. Experimental results show that FPO is able to provide excellent performance of global exploration and local minima avoidance simultaneously. However, to the higher dimensionality of multimodal problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve the performance of DFPO. A method of team size selection is proposed in DFPO-r to increase population diversity. The population diversity is one of the most important factors that determines the performance of the optimization algorithm. A higher degree of population diversity is able to help DFPO-r alleviate a premature convergence. The strategy of selection is to choose team size according to the higher degree of population diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100 dimensions to investigate the performance of DFPO and DFPO-r compared with LBest PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows better robustness performance compared with DFPO in experimental study.

Keywords

  • swarm intelligence
  • sitness predator optimizer
  • dynamic virtual team
  • population diversity
Open Access

Modelling Uncertainties in Multi-Criteria Decision Making using Distance Measure and TOPSIS for Hesitant Fuzzy Sets

Published Online: 23 Feb 2017
Page range: 103 - 109

Abstract

Abstract

A notion for distance between hesitant fuzzy data is given. Using this new distance notion, we propose the technique for order preference by similarity to ideal solution for hesitant fuzzy sets and a new approach in modelling uncertainties. An illustrative example is constructed to show the feasibility and practicality of the new method.

Keywords

  • uncertainty modelling
  • multiple criteria analysis
  • group decisions and negotiations
  • hesitant fuzzy set
  • TOPSIS

MSC 2010

  • 91B06
  • 91B10
  • 03B52
  • 03E72
  • 68T37
Open Access

Rule Based Networks: An Efficient and Interpretable Representation of Computational Models

Published Online: 23 Feb 2017
Page range: 111 - 123

Abstract

Abstract

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

Keywords

  • rule based networks
  • knowledge discovery
  • predictive modelling
  • rule representation
Open Access

A Novel Deep Neural Network that Uses Space-Time Features for Tracking and Recognizing a Moving Object

Published Online: 23 Feb 2017
Page range: 125 - 136

Abstract

Abstract

This work proposes a deep neural net (DNN) that accomplishes the reliable visual recognition of a chosen object captured with a webcam and moving in a 3D space. Autoencoding and substitutional reality are used to train a shallow net until it achieves zero tracking error in a discrete ambient. This trained individual is set to work in a real world closed loop system where images coming from a webcam produce displacement information for a moving region of interest (ROI) inside the own image. This loop gives rise to an emergent tracking behavior which creates a self-maintain flow of compressed space-time data. Next, short term memory elements are set to play a key role by creating new representations in terms of a space-time matrix. The obtained representations are delivery as input to a second shallow network which acts as “recognizer”. A noise balanced learning method is used to fast train the recognizer with real-world images, giving rise to a simple and yet powerful robotic eye, with a slender neural processor that vigorously tracks and recognizes the chosen object. The system has been tested with real images in real time.

Keywords

  • deep architectures
  • deep learning
  • artificial vision
Open Access

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Published Online: 23 Feb 2017
Page range: 137 - 147

Abstract

Abstract

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

Keywords

  • pattern recognition
  • fuzzy classification
  • complexity reduction
5 Articles
Open Access

Swarm Intelligence Algorithm Based on Competitive Predators with Dynamic Virtual Teams

Published Online: 23 Feb 2017
Page range: 87 - 101

Abstract

Abstract

In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature convergence for multimodal problems. In FPO, all of the particles are seen as predators. Only the competitive, powerful predator that are selected as an elite could achieve the limited opportunity to update. The elite generation with roulette wheel selection could increase individual independence and reduce rapid social collaboration. Experimental results show that FPO is able to provide excellent performance of global exploration and local minima avoidance simultaneously. However, to the higher dimensionality of multimodal problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve the performance of DFPO. A method of team size selection is proposed in DFPO-r to increase population diversity. The population diversity is one of the most important factors that determines the performance of the optimization algorithm. A higher degree of population diversity is able to help DFPO-r alleviate a premature convergence. The strategy of selection is to choose team size according to the higher degree of population diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100 dimensions to investigate the performance of DFPO and DFPO-r compared with LBest PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows better robustness performance compared with DFPO in experimental study.

Keywords

  • swarm intelligence
  • sitness predator optimizer
  • dynamic virtual team
  • population diversity
Open Access

Modelling Uncertainties in Multi-Criteria Decision Making using Distance Measure and TOPSIS for Hesitant Fuzzy Sets

Published Online: 23 Feb 2017
Page range: 103 - 109

Abstract

Abstract

A notion for distance between hesitant fuzzy data is given. Using this new distance notion, we propose the technique for order preference by similarity to ideal solution for hesitant fuzzy sets and a new approach in modelling uncertainties. An illustrative example is constructed to show the feasibility and practicality of the new method.

Keywords

  • uncertainty modelling
  • multiple criteria analysis
  • group decisions and negotiations
  • hesitant fuzzy set
  • TOPSIS

MSC 2010

  • 91B06
  • 91B10
  • 03B52
  • 03E72
  • 68T37
Open Access

Rule Based Networks: An Efficient and Interpretable Representation of Computational Models

Published Online: 23 Feb 2017
Page range: 111 - 123

Abstract

Abstract

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

Keywords

  • rule based networks
  • knowledge discovery
  • predictive modelling
  • rule representation
Open Access

A Novel Deep Neural Network that Uses Space-Time Features for Tracking and Recognizing a Moving Object

Published Online: 23 Feb 2017
Page range: 125 - 136

Abstract

Abstract

This work proposes a deep neural net (DNN) that accomplishes the reliable visual recognition of a chosen object captured with a webcam and moving in a 3D space. Autoencoding and substitutional reality are used to train a shallow net until it achieves zero tracking error in a discrete ambient. This trained individual is set to work in a real world closed loop system where images coming from a webcam produce displacement information for a moving region of interest (ROI) inside the own image. This loop gives rise to an emergent tracking behavior which creates a self-maintain flow of compressed space-time data. Next, short term memory elements are set to play a key role by creating new representations in terms of a space-time matrix. The obtained representations are delivery as input to a second shallow network which acts as “recognizer”. A noise balanced learning method is used to fast train the recognizer with real-world images, giving rise to a simple and yet powerful robotic eye, with a slender neural processor that vigorously tracks and recognizes the chosen object. The system has been tested with real images in real time.

Keywords

  • deep architectures
  • deep learning
  • artificial vision
Open Access

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Published Online: 23 Feb 2017
Page range: 137 - 147

Abstract

Abstract

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

Keywords

  • pattern recognition
  • fuzzy classification
  • complexity reduction