Name
Abstract | ||
|---|---|---|
Text classification is becoming an interesting research field due to increased availability of documents in digital form which is necessary to organize. The machine learning paradigm is usually applied to text classification, according to which a general inductive process automatically builds an text classifier from a set of pre-classified documents. In this paper we investigate the application of Bayesian networks to classify MedLine documents, where each document is identified by a set of MeSH ontology terms. Bayesian networks have been selected for their ability to describe conditional independencies between variables and provide clear methodologies for learning from observations.Our experimental evaluation of these ideas is based on the relevance judgments of the 2004 TREC workshop Genomics track. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02481-8_141 | IWANN (2) |
Keywords | Field | DocType |
clear methodology,digital form,text classifier,experimental evaluation,bayesian network,mesh terms,conditional independency,text classification,medline documents,trec workshop genomics track,mesh ontology term,medline document,conditional independence,machine learning | Data mining,Ontology,Information retrieval,Computer science,Bayesian network,Artificial intelligence,Classifier (linguistics),MEDLINE,Machine learning | Conference |
Volume | ISSN | Citations |
5518 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Glez-Peña | 1 | 172 | 25.03 |
| Sira López | 2 | 1 | 0.35 |
| Reyes Pavón | 3 | 57 | 8.08 |
| Rosalia Laza | 4 | 131 | 14.52 |
| Eva L. Iglesias | 5 | 42 | 2.78 |
| Lourdes Borrajo | 6 | 19 | 5.70 |