Guo Y. and Sutiwaraphun J., Knowledge probing in distributed Data Mining, in Proc. 4h Int. Conf. Knowledge Discovery Data Mining, pp 61-69, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....studying and understanding the various pruning methods and for interpreting meta classifiers. Other related methods for describing and computing comprehensible models of ensemble meta classifiers have been studied in the contexts of Knowledge Acquisition [ Domingos, 1997 ] and Knowledge Probing [ Guo Sutiwaraphun, 1998 ] 6.1.2 Correlation Metric and Pruning Contrary to the diversity metric of Section 5.2.1, the correlation metric measures the degree of similarity between a pair of classifiers. Given K 1 classifiers C 1 , C 2 , CK and C # and a data set of n examples mapped onto m classes, we can ....

Guo, Y., and Sutiwaraphun, J. 1998. Knowledge probing in distributed data mining. In H. Kargupta, P. C., ed., Work. Notes KDD-98 Workshop on Distributed Data Mining, 61--69. AAAI Press.

....techniques in knowledge discovery, such as data cleaning and preprocessing, transformation, and learning. Grossman et al. 23] outlined fundamental challenges for mining large sale databases, with one of them being the need to develop distributed data mining algorithms. Guo and Sutiwaraphun [24] described a meta learning concept named Knowledge Probing to distributed data mining. In Knowledge Probing, supervised learning is organized into two stages. At the first stage, a set of base classifiers is learned in parallel from a distributed data set. At the second stage, the relationship ....

Y. Guo and J. Sutiwaraphun, "Knowledge probing in distributed data mining," in Proc. 4h Int. Conf. Knowledge Discovery Data Mining, 1988, http://www.eecs.wsu.edu/ hillol/kdd98ws.html.

....the algorithm generates an alternative and more declarative representation that we can inspect. Other related methods for describing and computing comprehensible models of ensemble meta classifiers have been studied in the contexts of Knowledge Acquisition (Domingos, 1997) Knowledge Probing (Guo and Sutiwaraphun, 1998) and meta classifier correlation based visualization tools (Prodromidis, Stolfo and Chan, 1999) Computing decision tree models as part of the post training pruning algorithm is not only useful for pruning or for explaining the behavior of the meta 10 Andreas L. Prodromidis and Salvatore J. ....

Guo, Y. and Sutiwaraphun, J. (1998), Knowledge probing in distributed data mining, in P. C.

....model, the algorithm generates an alternative and more declarative representation that we can inspect. Other related methods for describing and computing comprehensible models of ensemble meta classifiers have been studied in the contexts of Knowledge Acquisition [11] Knowledge Probing [15] and meta classifier correlation based visualization tools [32] Computing decision tree models as part of the post training pruning algorithm are not only useful for pruning or for explaining the behavior of the meta classifier. These intermediate models are also meta classifiers and hence can ....

Y. Guo and J. Sutiwaraphun. Knowledge probing in distributed data mining. In P. Chan H. Kargupta, editor, Work. Notes KDD-98 Workshop on Distributed Data Mining, pages 61--69. AAAI Press, 1998.

....concepts, and the meta level learning may be applied recursively, producing a hierarchy of meta classi ers. The JAM system [36] is a meta learning based distributed data mining framework. It has been used for fraud detection in the banking domain [27] The knowledge probing approach is reported in [14]. This technique is similar to meta learning. However this approach is particularly designed for inducing descriptive data model from the predictions of black box classi ers learned in a distributed environment. The Distributed cooperative Bayesian learning approach was developed in [42] This ....

Y. Guo and J. Sutiwaraphun. Knowledge probing in distributed data mining. In Advances in Distributed and Parallel Knowledge Discovery, page Not available. AAAI/MIT Press, 1999.

....several methods for evaluating, composing and pruning hybrid classifiers that reduce their size while preserving or even improving their predictive performance. A quite different approach to creating comprehensible classifiers from ensembles is taken by Craven (1996) by Domingos (1997) and by Guo and Sutiwaraphun (1998). These authors use machine learning algorithms to induce understandable models of complex learned classification systems (Craven 1996) Specifically, they use the predictions of the ensemble as training labels, and learn from them a decision tree that models the hybrid s performance (with ....

Guo, Y. and J. Sutiwaraphun (1998). Knowledge probing in distributed data mining. In Working Notes of the KDD-97 Workshop on Distributed Data Mining, pp. 61--69.

....model, the algorithm generates an alternative and more declarative representation that we can inspect. Other related methods for describing and computing comprehensible models of ensemble meta classifiers have been studied in the contexts of Knowledge Acquisition [9] Knowledge Probing [12] and meta classifier correlation based visualization tools [22] Computing decision tree models as part of the post training pruning algorithm are not only useful for pruning or for explaining the behavior of the meta classifier. These intermediate models are also meta classifiers and hence can ....

Y. Guo and J. Sutiwaraphun. Knowledge probing in distributed data mining. In P. Chan H. Kargupta, editor, Work. Notes KDD-98 Workshop on Distributed Data Mining, pages 61-- 69. AAAI Press, 1998.

....several methods for evaluating, composing and pruning hybrid classifiers that reduce their size while preserving or even improving their predictive performance. A quite different approach to creating comprehensible classifiers from ensembles is taken by Craven (1996) by Domingos (1997) and by Guo and Sutiwaraphun (1998). These authors use machine learning algorithms to induce understandable models of complex learned classification systems (Craven 1996) cf. inducing understandable models of complex hand crafted classification systems (Danyluk and Provost 1993b) Specifically, they use the predictions of the ....

Guo, Y. and J. Sutiwaraphun (1998). Knowledge probing in distributed data mining. In Working Notes of the KDD-97 Workshop on Distributed Data Mining, pp. 61--69.

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Guo Y. and Sutiwaraphun J., Knowledge probing in distributed Data Mining, in Proc. 4h Int. Conf. Knowledge Discovery Data Mining, pp 61-69, 1998.

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