On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Active rules for sensor databases
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Grid-JQA — A New Architecture for QoS-Guaranteed Grid Computing System
PDP '06 Proceedings of the 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
Grid-JQA: grid Java based quality of service management by active database
ACSW Frontiers '06 Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data Mining Used in Rule Design for Active Database Systems
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
An approach to grid resource selection and fault management based on ECA rules
Future Generation Computer Systems
Resource allocation on computational grids using a utility model and the knapsack problem
Future Generation Computer Systems
Learning decision trees with taxonomy of propositionalized attributes
Pattern Recognition
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Active Grid Information Server for grid computing
The Journal of Supercomputing
An improved CART decision tree for datasets with irrelevant feature
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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Grid computing is becoming a mainstream technology for large-scale resource sharing and distributed system integration. One underlying challenge in Grid computing is the resource management. In this paper, active rule learning is considered for resource management in Grid computing. Rule learning is very important for updating rules in an active database system. However, it is also very difficult because of a lack of methodology and support. A decision tree can be used in rule learning to cope with the problems arising in active semantic extraction, termination analysis of the rule set and rule updates. Also our aim in rule learning is to learn new attributes in rules, such as time and load balancing, in regard to instances of a real Grid environment that a decision tree can provide. In our work, a set of decision trees is built in parallel on training data sets based on the original rule set. Each learned decision tree can be reduced to a set of rules and thence conflicting rules can be resolved. Results from cross validation experiments on a data set suggest this approach may be effectively applied for rule learning.