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Overall, cautious behavior for collective inference is highly effective. Within an algorithm family, ICC or PGCC, cautious settings improved performance. In particular, the gains were larger and statistically significant for ICC. Also, the most cautious ICC (ICCU/Tr/C) outperformed the best PGCC.
We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds. Keywords:.
We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds. 1 Introduction. We are interested in the ...
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Many collective classification (CC) algorithms have been shown to increase accuracy when in- stances are interrelated. However, CC algorithms must be ...
Abstract. Collective classification can significantly improve accuracy by exploiting relationships among instances. Although several collective inference ...
The algorithm makes an initial label inference y i for each v i , then iteratively re-estimate the labels based on the inferences of every participant that is ...
It is conjecture that cautious approaches that identify and preferentially exploit the more reliable intermediate data should outperform aggressive ...
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In this paper we propose an alternative using cautious inference. Building on ideas from Collective Classification, we favor the most confident hypotheses as ...
Cautious Rule-Based Collective Inference. M Svatoš. Proceedings of the 28th International Joint Conference on Artificial …, 2019. 2019 ; Structure learning of ...
First, we describe cautious inference and explain how four well-known families of CC algorithms can be parameterized to use varying degrees of such caution.
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