Using Semantic Data Mining for Classification Improvement and Knowledge Extraction
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The objective of this position paper is to show that the inte- gration of semantic data mining into the DAMIART data mining system can help further improve classification performance and knowledge ex- traction. DAMIART performs multi-label classification in the presence of multiple class ontologies, hierarchy extraction from multi-labels and concept relation by association rule mining. Whereas DAMIART com- bines knowledge from multiple data sources and multiple class ontologies, the proposed extension should also explore available ontologies over at- tributes. This will allow the system to produce not only more accurate classification results but also improve their interpretability and overcome such problems as data sparseness.
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BENITES, Fernando, Elena SAPOZHNIKOVA, 2014. Using Semantic Data Mining for Classification Improvement and Knowledge Extraction. KDML. Aachen, Germany, 8. Sept. 2014 - 12. Sept. 2014. In: SEIDL, Thomas, ed., Marwan HASSANI, ed., Christian BEECKS, ed.. Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014. CEUR-WS.org, 2014, pp. 150-155. CEUR Workshop Proceedings. 1226BibTex
@inproceedings{Benites2014Using-29338, year={2014}, title={Using Semantic Data Mining for Classification Improvement and Knowledge Extraction}, number={1226}, publisher={CEUR-WS.org}, series={CEUR Workshop Proceedings}, booktitle={Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014}, pages={150--155}, editor={Seidl, Thomas and Hassani, Marwan and Beecks, Christian}, author={Benites, Fernando and Sapozhnikova, Elena} }
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