Name
Abstract | ||
|---|---|---|
Recently, categorization methods based on association rules have been given much attention. In general, association classification has the higher accuracy and the better performance. However, the classification accuracy drops rapidly when the distribution of feature words in training set is uneven. Therefore, text categorization algorithm Weighted Association Rules Categorization (WARC) is proposed in this paper. In this method, association rules are used to classify training samples and rule intensity is defined according to the number of misclassified training samples. Each strong rule is multiplied by factor less than 1 to reduce its weight while each weak rule is multiplied by factor more than 1 to increase its weight. The result of research shows that this method can remarkably improve the accuracy of association classification algorithms by regulation of rules weights. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11527503_43 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
strong rule,rule intensity,training set,rules weight,association classification algorithm,classification accuracy,text association classification,association rule,association classification,higher accuracy,training sample,information retrieval,data mining,classification,content analysis,knowledge base,expert system,statistical association,categorization,linguistics,data analysis | Training set,Data mining,Categorization,Pattern recognition,Computer science,Expert system,Association rule learning,Correlation and dependence,Artificial intelligence,Statistical classification,Text categorization,Machine learning | Conference |
Volume | ISSN | ISBN |
3584 | 0302-9743 | 3-540-27894-X |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Yun Chen | 1 | 6 | 1.50 |
| Yi Chen | 2 | 0 | 0.68 |
| Ronglu Li | 3 | 27 | 2.97 |
| Yunfa Hu | 4 | 74 | 13.44 |