頁籤選單縮合
題 名 | Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces |
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作 者 | Ye, Yunming; Li, Hongbo; Deng, Xiaobai; Huang, Joshua Zhexue; | 書刊名 | International Journal of Computational Linguistics & Chinese Language Processing |
卷 期 | 13:4 2008.12[民97.12] |
頁 次 | 頁387-404 |
分類號 | 312.2 |
關鍵詞 | Search interface detection; Random forest; Hidden web; Form classification; |
語 文 | 英文(English) |
英文摘要 | Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weighted feature selection method to build the individual classifiers. With this improved random forest algorithm (IRFA), each classifier can be learned from a weighted subset of the feature space so that the ensemble of decision trees can fully exploit the useful features of search interface patterns. We have compared our ensemble approach with other well-known classification algorithms, such as SVM, C4.5, Naïve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more effective in detecting search interfaces of the hidden Web. |
本系統中英文摘要資訊取自各篇刊載內容。