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Paper Title:
Web Page Classification with Heterogeneous Data Fusion


Web pages are more than text and they contain much contextual and structural information, e.g., the title, meta data, the anchor text, etc., each of which can be seen as a data source or a representation. Due to the different dimensionality and different representing forms of these heterogeneous data sources, simply putting them together would not greatly enhance the classification performance. We observe that via a kernel function, different dimensions and types of data sources can be represented into a common format of kernel matrix, which can be seen as a generalized similarity measure between web pages. In this sense, a kernel learning approach is employed to fuse these heterogeneous data sources. The experimental results on a collection of the ODP database validate the advantages of the proposed method over any single data source and the uniformly weighted combination of heterogeneous data sources.

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