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Using Bayesian Classifiers to Combine Rules
| Content Provider | Semantic Scholar |
|---|---|
| Author | Davis, Jesse Costa, Vítor Santos Ong, Irene M. Page, David Dutra, Inês De Castro |
| Copyright Year | 2004 |
| Abstract | One of the most popular techniques for multi-relational data mining is Inductive Logic Programming (ILP). Given a set of positive and negative examples, an ILP system ideally finds a logical description of the underlying data model that discriminates the positive examples from the negative examples. However, in multi-relational data mining, one often has to deal with erroneous and missing information. ILP systems can still be useful by generating rules that captures the main relationships in the system. An important question is how to combine these rules to form an accurate classifier. An interesting approach to this problem is to use Bayes Net based classifiers. We compare Näıve Bayes, Tree Augmented Näıve Bayes (TAN) and the Sparse Candidate algorithm to a voting classifier. We also show that a full classifier can be implemented as a CLP(BN ) program [14], giving some insight on how to pursue further improvements. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.wisc.edu/~dpage/nbayes.pdf |
| Alternate Webpage(s) | http://www.cs.wisc.edu/~jdavis/nbayes.pdf |
| Alternate Webpage(s) | http://www-ai.ijs.si/SasoDzeroski/MRDM2004/proceedings/davis.pdf |
| Alternate Webpage(s) | http://www.cos.ufrj.br/~ines/papers/nbayes.ps |
| Alternate Webpage(s) | http://pages.cs.wisc.edu/~dpage/nbayes.pdf |
| Alternate Webpage(s) | http://pages.cs.wisc.edu/~jdavis/nbayes.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
