The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 20-264
Full Name: Manuel Santos
Position: Associate Professor
Age: ON
Sex: Male
Address: Departamento de Sistemas de Informação, Escola de Engenharia, Universidade do Minho
Country: PORTUGAL
Tel: +351253510306
Tel prefix:
Fax: +351253510300
E-mail address: mfs@dsi.uminho.pt
Other E-mails:
Title of the Paper: Grid Learning Classifiers - A Web Based Interface
Authors as they appear in the Paper: Manuel Filipe Santos, Wesley Mathew, Henrique Santos
Email addresses of all the authors: mfs@dsi.uminho.pt,wesley@dsi.uminho.pt,hsantos@dsi.uminho.pt
Number of paper pages: 9
Abstract: The toolkit for learning classifier system for grid data mining is a communication channel between remote users and gridclass system. Gridclass system is the system for grid data mining, grid computing approach in the distributed data mining. This toolkit is a web based system therefore end users can set the configuration of each node in the grid environment and execute the grid class system from the remote location. Mainly, configuration module of the toolkit is designed for the sUpervised Classifier System (UCS) as a data mining algorithm. Toolkit has three fundamental functions such as creating new project, updating the project, and executing the project. Initially, user has to define the project based on the complexity of the problem to the system. While creating a new project all the data and configuration information about all nods are stored in the file under a user defined project name. The updating phase user can makes changes in the configuration file or !
replace the training data for new experiments. There are two sub functions in the phase of execution: do the execution of gridclass system and do the comparison and evaluation of the performance of the different executions. Toolkit can store the global model and related local models and it testing accuracies in the server system. The main focus of this work is to improve the performance of learning classifier system: therefore an attempt is made to compare the performance of learning classifier system with different configurations, which has a significant role. The ROC graph is the best option to represent the performance of classifier system. Accuracy under the curve (ACU) is a numerical value to represent the ROC curve. Therefore, users can easy to measure the performance of global model with the help of AUC. Other objective of this work is to provide friendly environment to the end users and gives better facilities to evaluate the performance of the global model.
Keywords: Gridclass system, Grid data mining, Supervised classifier system, Toolkit
EXTENSION of the file: .doc
Special (Invited) Session: GridclassTK – Toolkit for grid learning classifier systems
Organizer of the Session: 104-093
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