Execution Engine of Meta-learning System for KDD in Multi-agent Environment

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

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Design and Development of a Multi-Agent E-Learning System

Special and Gifted Education ◽

10.4018/978-1-5225-0034-6.ch016 ◽

2016 ◽

pp. 390-447

Author(s):

Terje Kristensen ◽

Marius Dyngeland

Keyword(s):

Software Development ◽

Service Oriented Architecture ◽

Learning System ◽

Agent Architecture ◽

Quality Requirements ◽

Client Server ◽

Dynamic Content ◽

Service Oriented ◽

E Learning ◽

Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

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Design and Development of a Multi-Agent E-Learning System

International Journal of Agent Technologies and Systems ◽

10.4018/ijats.2015040102 ◽

2015 ◽

Vol 7 (2) ◽

pp. 19-74 ◽

Cited By ~ 1

Author(s):

Terje Kristensen ◽

Marius Dyngeland

Keyword(s):

Software Development ◽

Service Oriented Architecture ◽

Learning System ◽

Agent Architecture ◽

Quality Requirements ◽

Client Server ◽

Dynamic Content ◽

Service Oriented ◽

E Learning ◽

Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

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Implementation of Parameter Space Search for Meta Learning in a Data-Mining Multi-agent System

2011 10th International Conference on Machine Learning and Applications and Workshops ◽

10.1109/icmla.2011.161 ◽

2011 ◽

Cited By ~ 5

Author(s):

O. Kazik ◽

Klara Peskova ◽

Martin Pilat ◽

R. Neruda

Keyword(s):

Data Mining ◽

Parameter Space ◽

Multi Agent System ◽

Agent System ◽

Meta Learning ◽

Multi Agent

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Analysis and Design on PBL System Based on Multi-Agent

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.2026 ◽

2011 ◽

Vol 474-476 ◽

pp. 2026-2031

Author(s):

Chun Fei Zhang ◽

Meng Yang Li ◽

Jiu Hong Wei ◽

Wan Long Li

Keyword(s):

Information Technology ◽

Problem Based Learning ◽

Logical Structure ◽

Learning System ◽

Agent Technology ◽

Teaching System ◽

Key Technology ◽

Analysis And Design ◽

Research On Teaching ◽

Multi Agent

Development of information technology provides the technical support for instruction. Research on teaching-system is developed using agent technology at present. A problem based learning system is structured using multi-Agent after the characteristics of PBL is analyzed. Each agent’s function and logical structure are discussed. Composition and workflow of the system are introduced. And the last, we also made some advanced research about the key technology.

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