Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combination of Multiple Bipartite Ranking for Web Content Quality
  Evaluation   Web content quality estimation is crucial to various web content processing
applications. Our previous work applied Bagging + C4.5 to achive the best
results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of
many point-wise rankinig models. In this paper, we combine multiple pair-wise
bipartite ranking learner to solve the multi-partite ranking problems for the
web quality estimation. In encoding stage, we present the ternary encoding and
the binary coding extending each rank value to $L - 1$ (L is the number of the
different ranking value). For the decoding, we discuss the combination of
multiple ranking results from multiple bipartite ranking models with the
predefined weighting and the adaptive weighting. The experiments on ECML/PKDD
2010 Discovery Challenge datasets show that \textit{binary coding} +
\textit{predefined weighting} yields the highest performance in all four
combinations and furthermore it is better than the best results reported in
ECML/PKDD 2010 Discovery Challenge competition.

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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: adap-org