In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can ...In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can usethe Internet to couple them together, their performance will be improved throughcooperation. This is because the problem-solving ability of expert systems is greatlyimproved by the way of cooperation among different expert systems in a distributedexpert system. Cooperation between different expert systems with these two het-erogeneous uncertain reasoning models is essentially based on the transformations ofuncertainties of propositions between these two models. In this paper, we discoveredthe exactly isomorphic transformations uncertainties between uncertain reasoningmodels, as used by EMYCIN and PROSPECTOR.展开更多
From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-c...From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which axe the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples axe covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.展开更多
文摘In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can usethe Internet to couple them together, their performance will be improved throughcooperation. This is because the problem-solving ability of expert systems is greatlyimproved by the way of cooperation among different expert systems in a distributedexpert system. Cooperation between different expert systems with these two het-erogeneous uncertain reasoning models is essentially based on the transformations ofuncertainties of propositions between these two models. In this paper, we discoveredthe exactly isomorphic transformations uncertainties between uncertain reasoningmodels, as used by EMYCIN and PROSPECTOR.
基金supported by Australian Research Council Linkage Project under Grant No. LP0775041the Early Career Researcher Grant under Grant No. 2007002448 from University of Technology, Sydney, Australia
文摘From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which axe the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples axe covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.
