Name
Abstract | ||
|---|---|---|
Classification is one of the most useful techniques for extracting meaningful knowledge from databases. Classifiers, e.g. decision trees, are usually extracted from a table of records, each of which represents an example. However, quite often in real applications there is other knowledge, e.g. owned by experts of the field, that can be usefully used in conjunction with the one hidden inside the examples. As a concrete example of this kind of knowledge we consider causal dependencies among the attributes of the data records. In this paper we discuss how to use such a knowledge to improve the construction of classifiers. The causal dependencies are represented via Bayesian Causal Maps (BCMs), and our method is implemented as an adaptation of the well known C4.5 algorithm. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1066677.1066787 | SAC |
Keywords | Field | DocType |
real application,improving c4,causal dependency,decision tree,bayesian causal maps,useful technique,prior knowledge,concrete example,data record,meaningful knowledge,optimization | Data mining,Decision tree,Computer science,Artificial intelligence,Machine learning,Data records,Bayesian probability | Conference |
ISBN | Citations | PageRank |
1-58113-964-0 | 12 | 0.90 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miriam Baglioni | 1 | 128 | 9.33 |
| Barbara Furletti | 2 | 80 | 7.87 |
| Franco Turini | 3 | 842 | 101.81 |