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• W2052022265 abstract "Despite the extensive number of studies for multimodal information fusion, the issue of determining the optimal modalities has not been adequately addressed yet. In this study, a RELIEF-based multimodal feature selection approach (RELIEF-RDR) is proposed. The original RELIEF algorithm is extended for weaknesses in three major issues; multi-labeled data, noise and class-specific feature selection. To overcome these weaknesses, discrimination based weighting mechanism of RELIEF is supported with two additional concepts; representation and reliability capabilities of features, without an increase in computational complexity. These capabilities of features are exploited by using the statistics on dissimilarities of training instances. The experiments conducted on TRECVID 2007 dataset validated the superiority of RELIEF-RDR over RELIEF." @default.
• W2052022265 created "2016-06-24" @default.
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• W2052022265 date "2012-06-05" @default.
• W2052022265 modified "2023-10-16" @default.
• W2052022265 title "A RELIEF-based modality weighting approach for multimodal information retrieval" @default.
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• W2052022265 doi "https://doi.org/10.1145/2324796.2324858" @default.
• W2052022265 hasPublicationYear "2012" @default.
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