Learning Rules that Classify E-Mail Export

@inproceedings{citeulike:5840302, abstract = {Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personal e-mail messages: a \&\#034;traditional IR\&\#034; method based on TF-IDF weighting, and a new method for learning sets of \&\#034;keyword-spotting rules\&\#034; based on the RIPPER rule learning algorithm. It is demonstrated that both methods obtain significant generalizations from a small number of examples; that both methods are comparable in generalization performance on problems of this type; and that both methods are reasonably efficient, even with fairly large training sets. However, the greater comprehensibility of the rules may be advantageous in a system that allows users to extend or otherwise modify a learned classifier. Introduction Perhaps the most-discussed technical phenomenon of recent years has been the rapid growth of the Internet---or more generally, the rapid growth in the number of on-line documents. This has led to increased interest in intelligent methods for ...}, author = {Cohen, William W.}, booktitle = {In Papers from the AAAI Spring Symposium on Machine Learning in Information Access}, citeulike-article-id = {5840302}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4129}, keywords = {classification, email, rule-based, stroz-friedberg, tf-idf}, pages = {18--25}, posted-at = {2009-09-25 15:39:45}, priority = {2}, title = {Learning Rules that Classify E-Mail}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4129}, year = {1996} }

TY - CONF ID - citeulike:5840302 L3 - citeulike-article-id:5840302 N2 - Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personal e-mail messages: a "traditional IR" method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rules" based on the RIPPER rule learning algorithm. It is demonstrated that both methods obtain significant generalizations from a small number of examples; that both methods are comparable in generalization performance on problems of this type; and that both methods are reasonably efficient, even with fairly large training sets. However, the greater comprehensibility of the rules may be advantageous in a system that allows users to extend or otherwise modify a learned classifier. Introduction Perhaps the most-discussed technical phenomenon of recent years has been the rapid growth of the Internet---or more generally, the rapid growth in the number of on-line documents. This has led to increased interest in intelligent methods for ... EP - 25 TI - Learning Rules that Classify E-Mail T2 - In Papers from the AAAI Spring Symposium on Machine Learning in Information Access SP - 18 KW - classification KW - email KW - rule-based KW - stroz-friedberg KW - tf-idf AU - Cohen, William PY - 1996/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4129 ER -