Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a n...Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.展开更多
Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub ...Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.展开更多
Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sha...Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items(attribute values)in the training data instances.In response to the above two issues,a new dynamic rule induction(DRI)method is proposed.Whenever a rule is produced and its related training data instances are discarded,DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion.Therefore,the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM.This enables DRI to generate near perfect rules and realistic classifiers.Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.展开更多
文摘Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.
文摘Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.
文摘Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items(attribute values)in the training data instances.In response to the above two issues,a new dynamic rule induction(DRI)method is proposed.Whenever a rule is produced and its related training data instances are discarded,DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion.Therefore,the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM.This enables DRI to generate near perfect rules and realistic classifiers.Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.
