Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Artificial Intelligence Review
Combining linguistic and structural descriptors for mining biomedical literature
Proceedings of the 2006 ACM symposium on Document engineering
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In the literature, it is commonly believed that learning from few data problem can be resolved by using classifiers that consider interclass relationships. In this work, we will adopt this point of view in learning from few sparse textual data, essentially, by considering the sparseness of the latter as a good support for inducing theories about generalization. Therefore, we opt for an inductive approach based on combining: evidence-based analysis of patterns, logic and preferences. More precisely, we are interested in supervised learning of biomedical articles by exploiting a multi-scale hybrid description and constrained pattern-based data mining techniques. Unlike existing works, we will highlight the relevance of the absence/weakness of patterns and we will associate to their absence a semantic value compared to their presence. The main characteristic of our approach is that of considering local and global contexts, which connect textual data by introducing regret ratio measures and generalized exclusive patterns in order to avoid a crisp effect between the absence and presence of patterns. Experimental results show the effectiveness of our approach.