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Conference papers
Statistically Significant Discriminative Patterns Searching
Abstract : In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its eciency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. Experiment on real data also shows that SSDPS eciently detects multiple SNPs combinations in genetic data.
Cited literature [12 references]
https://hal.archives-ouvertes.fr/hal-02190793
Contributor : Alexandre Termier <>
Submitted on : Monday, July 22, 2019 - 9:30:18 PM
Last modification on : Thursday, January 7, 2021 - 4:35:10 PM
Contributor : Alexandre Termier <>
Submitted on : Monday, July 22, 2019 - 9:30:18 PM
Last modification on : Thursday, January 7, 2021 - 4:35:10 PM
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paper_SSDPS_DaWak19_final.pdf
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