On the suitability of combining feature selection and resampling to manage data complexity
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
On the suitability of combining feature selection and resampling to manage data complexityDate
2010Publisher
Springer VerlagISSN
0302-9743Bibliographic citation
Martín-Félez R., Mollineda R.A. (2010) On the Suitability of Combining Feature Selection and Resampling to Manage Data Complexity. In: Meseguer P., Mandow L., Gasca R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science, vol 5988. SpringerType
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/chapter/10.1007/978-3-642-14264-2_15Version
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Abstract
The effectiveness of a learning task depends on data com- plexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should ... [+]
The effectiveness of a learning task depends on data com- plexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should be applied. Nevertheless, no much effort has been de- voted to investigate the importance of the order in which they can be used. This paper focuses on the joint use of feature reduction and bal- ancing techniques, and studies which could be the application order that leads to the best classification results. This analysis was made on a spe- cific problem whose aim was to identify the melodic track given a MIDI file. Several experiments were performed from different imbalanced 38- dimensional training sets with many more accompaniment tracks than melodic tracks, and where features were aggregated without any correla- tion study. Results showed that the most effective combination was the ordered use of resampling and feature reduction techniques. [-]
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Lecture notes in computer science, vol. 5988 (2010)Rights
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