1. Web Page Classification with Heterogeneous Data Fusion
The Chinese University of Hong Kong
Web Page Classification with Heterogeneous Data Fusion
Zenglin Xu, Irwin King and Michael R. Lyu
Department of Computer Science and Engineering
The Chinese University of Hong Kong
{zlxu, king, 1
Motivations 2
Contributions
For web page classification, there are many available data
sources, such as the text, the title, the meta data, the anchor
text, etc.
Simply putting them together would not greatly enhance the
classification performance.
Different dimensions and types of data sources can be
represented into a common format of kernel matrix.
A kernel learning approach is thus proposed to integrate
multiple data sources
A systematic way of integrating multiple
data sources.
Better classification accuracy. 3
Architacture & Model
1. Feature Extraction.
2. Similarity Representation. Each data source is represented as a kernel matrix (Ki)
3. Similarity Combination.
4. Classification.
Substitute K into the dual SVM
We have the following QCQP problem:
where αis the parameter of dual SVMs,δ is a constant and t is the trace vector. 4
Experiment results
Dataset: DMOZ
AT: Anchor Text
LT: Link Text
MT: Meta Data
TI: Title
PT: Plain Text
UW: Universally Weighted sources
KC: sources by Kernel Combination
Mi -F1: Micro-F1
Ma-F1: Macro-F1
The Chinese University of Hong Kong
WWW 2007, May 8–12, 2007, Banff, Alberta, Canada.