Feature Extraction for Machine Learning: Logic-Probabilistic Approach

Vladimir Gorodetsky, Vladimir Samoylov
; Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:55-65, 2010.

Abstract

The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly such learning data structures are peculiar to many novel challenging applications. The paper proposes a new technology supported by a number of novel algorithms intended for ontology-centered transformation of heterogeneous possibly poor structured learning data into homogeneous informative binary feature space based on (1) aggregation of the ontology notion instances and their attribute domains and subsequent probabilistic cause-consequence analysis aimed at extraction more informative features. The proposed technology is fully implemented and validated on several case studies.

Cite this Paper


BibTeX
@InProceedings{pmlr-v10-gorodetsky10a, title = {Feature Extraction for Machine Learning: Logic-Probabilistic Approach}, author = {Vladimir Gorodetsky and Vladimir Samoylov}, pages = {55--65}, year = {2010}, editor = {Huan Liu and Hiroshi Motoda and Rudy Setiono and Zheng Zhao}, volume = {10}, series = {Proceedings of Machine Learning Research}, address = {Hyderabad, India}, month = {21 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v10/gorodetsky10a/gorodetsky10a.pdf}, url = {http://proceedings.mlr.press/v10/gorodetsky10a.html}, abstract = {The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly such learning data structures are peculiar to many novel challenging applications. The paper proposes a new technology supported by a number of novel algorithms intended for ontology-centered transformation of heterogeneous possibly poor structured learning data into homogeneous informative binary feature space based on (1) aggregation of the ontology notion instances and their attribute domains and subsequent probabilistic cause-consequence analysis aimed at extraction more informative features. The proposed technology is fully implemented and validated on several case studies.} }
Endnote
%0 Conference Paper %T Feature Extraction for Machine Learning: Logic-Probabilistic Approach %A Vladimir Gorodetsky %A Vladimir Samoylov %B Proceedings of the Fourth International Workshop on Feature Selection in Data Mining %C Proceedings of Machine Learning Research %D 2010 %E Huan Liu %E Hiroshi Motoda %E Rudy Setiono %E Zheng Zhao %F pmlr-v10-gorodetsky10a %I PMLR %J Proceedings of Machine Learning Research %P 55--65 %U http://proceedings.mlr.press %V 10 %W PMLR %X The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly such learning data structures are peculiar to many novel challenging applications. The paper proposes a new technology supported by a number of novel algorithms intended for ontology-centered transformation of heterogeneous possibly poor structured learning data into homogeneous informative binary feature space based on (1) aggregation of the ontology notion instances and their attribute domains and subsequent probabilistic cause-consequence analysis aimed at extraction more informative features. The proposed technology is fully implemented and validated on several case studies.
RIS
TY - CPAPER TI - Feature Extraction for Machine Learning: Logic-Probabilistic Approach AU - Vladimir Gorodetsky AU - Vladimir Samoylov BT - Proceedings of the Fourth International Workshop on Feature Selection in Data Mining PY - 2010/05/26 DA - 2010/05/26 ED - Huan Liu ED - Hiroshi Motoda ED - Rudy Setiono ED - Zheng Zhao ID - pmlr-v10-gorodetsky10a PB - PMLR SP - 55 DP - PMLR EP - 65 L1 - http://proceedings.mlr.press/v10/gorodetsky10a/gorodetsky10a.pdf UR - http://proceedings.mlr.press/v10/gorodetsky10a.html AB - The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly such learning data structures are peculiar to many novel challenging applications. The paper proposes a new technology supported by a number of novel algorithms intended for ontology-centered transformation of heterogeneous possibly poor structured learning data into homogeneous informative binary feature space based on (1) aggregation of the ontology notion instances and their attribute domains and subsequent probabilistic cause-consequence analysis aimed at extraction more informative features. The proposed technology is fully implemented and validated on several case studies. ER -
APA
Gorodetsky, V. & Samoylov, V.. (2010). Feature Extraction for Machine Learning: Logic-Probabilistic Approach. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, in PMLR 10:55-65

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