Name
Abstract | ||
|---|---|---|
In the bag of words framework, documents are often converted into vectors according to predefined features together with weighting mechanisms. Since each feature presentation has its character, it is difficult to determine which one should be chosen for a specific domain, especially for the users who are not familiar with the domain. This paper explores the integration of various feature presentations to improve the classification accuracy. A general two phases framework is proposed. In the first phase, we train multiple base classifiers with various vector spaces and use these classifiers to predict the class of testing samples respectively. In the second phase, the previous predicted results are integrated into the ultimate class via stacking with SVM. The experimental results demonstrate the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2187980.2188131 | WWW (Companion Volume) |
Keywords | Field | DocType |
various vector space,specific domain,classification accuracy,feature presentation,ultimate class,phases framework,multiple feature presentation,predefined feature,words framework,sentiment classification,various feature presentation,vector space,bag of words | Bag-of-words model,Data mining,Vector space,Weighting,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Linear classifier,Machine learning,Stacking | Conference |
Citations | PageRank | References |
4 | 0.41 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuming Lin | 1 | 37 | 4.76 |
| Jingwei Zhang | 2 | 27 | 2.02 |
| Xiaoling Wang | 3 | 469 | 72.53 |
| Aoying Zhou | 4 | 2632 | 238.85 |