Prodromidis, A. L., Stolfo, S. J. and Chan, P. K., E#ective and e#cient pruning of metaclassifiers in a distributed data mining system. Technical report CUCS-017-99, Columbia Univ., 1999. Home/Search Document Not in Database Summary Related Articles Check |
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Distributed Data Mining Systems - Prodromidis (1999) Self-citation (Prodromidis) (Correct)
....change over time) extensible to support new and more advanced data mining technologies and, last but not least, highly accurate. The focus of this thesis is to identify and describe each of these issues separately and to detail our approaches within the framework of JAM [ Prodromidis, 1997; Prodromidis, Chan, Stolfo, 1999 ] JAM has been used in several experiments dealing with real world learning tasks, such as solving crucial problems in fraud detection in financial information systems [ Stolfo et al. 1998; Chan et al. 1999 ] The objective here, is to employ pattern directed inference systems using models ....
....Briefly, JAM provides a set of learning programs, implemented or wrapped within JAVA agents, that compute models (or classifiers) over data stored locally at a site. JAM also provides a set of meta learning agents for combining multiple models that were learned (perhaps) at di#erent sites [ Prodromidis Stolfo, 1999c ] Furthermore, it employs a special distribution mechanism that allows the migration of the derived models or classifier agents to other remote sites. Figure 1.1 depicts the JAM system with three data sites, Orange, Mango and Strawberry while exchanging their classifier agents. In this ....
[Article contains additional citation context not shown here]
Prodromidis, A. L.; Stolfo, S. J.; and Chan, P. K. 1999. E#ective and e#cient pruning of meta-classifiers in a distributed data mining system. Technical report, Columbia Univ. CUCS-017-99.
Cost Complexity-based Pruning of Ensemble Classifiers - Prodromidis, Stolfo (1999) (6 citations) Self-citation (Prodromidis Stolfo) (Correct)
....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. Stolfo classifier. These intermediate models are also meta classifiers and hence can also be used ....
....on the evaluation of the post training pruning algorithm as a general method for reducing the size of an ensemble metaclassifier. Detailed information on e#ective fraud detectors with extensive results (TP FP spread and cost model) from the mining of these credit card data sets can be found in (Prodromidis and Stolfo, 1999a) 4.1. Computing Base Classifiers The first step involves the training of the base classifiers. We split each data set in 12 subsets (each subset corresponding to one month s data) and distribute them across six di#erent data sites (each site storing two subsets) Then we apply the 5 learning ....
[Article contains additional citation context not shown here]
Prodromidis, A. L., Stolfo, S. J. and Chan, P. K. (1999), E#ective and e#cient pruning of meta-classifiers in a distributed data mining system, Technical report, Columbia Univ.
Cost Complexity Pruning of Ensemble Classifiers - Prodromidis, Stolfo Self-citation (Prodromidis Stolfo) (Correct)
....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 too be used to classify unlabeled instances. Recall, that they ....
A. L. Prodromidis, S. J. Stolfo, and P. K. Chan. E#ective and e#cient pruning of meta-classifiers in a distributed data mining system. Technical report, Columbia Univ., 1999. CUCS-017-99.
Cost Complexity-based Pruning of Ensemble Classifiers - Prodromidis, Stolfo (1999) (6 citations) Self-citation (Prodromidis Stolfo) (Correct)
....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 too be used to classify unlabeled instances. Recall, that they ....
A. L. Prodromidis, S. J. Stolfo, and P. K. Chan. E#ective and e#cient pruning of metaclassifiers in a distributed data mining system. Knowledge Discovery and Data Mining Journal. submitted for publication.
Meta-Learning in Distributed Data Mining Systems: Issues.. - Prodromidis, Chan, al. (2000) (34 citations) Self-citation (Prodromidis Stolfo Chan) (Correct)
....pruning technique (a technique used by the CART decision tree learning algorithm [4] that seeks to minimize the cost size of its tree while reducing the misclassification rate) and the second on the correlation between the classifiers and the meta classifier. Both algorithms are detailed in [46]. There are two primary objectives for the pruning techniques: 1. to acquire and combine information from multiple databases in a timely manner 2. to generate e#ective and e#cient meta classifiers. These pre training pruning techniques preceed the meta learning phase and, as such, can be used in ....
....would not be tested unfairly on known data. Specifically, we had each site use half of its local data (one month) to test, prune and meta learn the base classifiers and the other half to evaluate the overall performance of the pruned or unpruned meta classifier (more details can be found in [45, 46]) In essence, the setting of this experiment corresponds to a parallel 6 fold cross validation. Finally, we had the two banks exchange their classifier agents as well. In addition to its 10 local and 50 internal classifiers (those imported from their peer data sites) each site also imported 60 ....
[Article contains additional citation context not shown here]
Andreas L. Prodromidis, Salvatore J. Stolfo, and Philip K. Chan. E#ective and e#cient pruning of meta-classifiers in a distributed data mining system. Knowledge Discovery and Data Mining Journal. submitted for publication.
Pattern Analysis and Applications manuscript No. - Will Be Inserted (Correct)
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Prodromidis, A. L., Stolfo, S. J. and Chan, P. K., E#ective and e#cient pruning of metaclassifiers in a distributed data mining system. Technical report CUCS-017-99, Columbia Univ., 1999.
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