Name
Abstract | ||
|---|---|---|
In this paper, we describe our approach for addressing Task 1 in the KDD CUP 2002 competition. The approach is based on developing and using an improved automatic feature selection method in conjunction with traditional classifiers. The feature selection method used is based on capturing frequently occurring keyword combinations (or motifs) within short segments of the text of a document and has proved to produce more accurate classification results than approaches relying solely on using keyword-based features. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1145/772862.772876 | SIGKDD Explorations |
Keywords | Field | DocType |
feature selection,keyword-based feature,traditional classifier,local pattern,feature selection method,improved automatic feature selection,svm,accurate classification result,short segment,document categorization,kdd cup,automatic scientific text classification,keyword combination | Data mining,Pattern recognition,Feature selection,Computer science,Support vector machine,Artificial intelligence | Journal |
Volume | Issue | Citations |
4 | 2 | 17 |
PageRank | References | Authors |
2.14 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moustafa Ghanem | 1 | 538 | 53.05 |
| Yike Guo | 2 | 1319 | 165.32 |
| Huma Lodhi | 3 | 854 | 84.61 |
| Yong Zhang | 4 | 17 | 2.14 |