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• W3091808987 abstract "Pattern Recognition tasks in the structural domain generally exhibit high accuracy results, but their time efficiency is quite low. Furthermore, this low performance is more pronounced when dealing with instance-based classifiers, since, for each query, the entire corpus must be evaluated to find the closest prototype. In this work we address this efficiency issue for the Nearest Neighbor classifier when data are encoded as two-dimensional code sequences, and more precisely strings and sequences of vectors. For this, a set of bounds is proposed in the distance metric that avoid the calculation of unnecessary distances. Results obtained prove the effectiveness of the proposal as it reduces the classification time in percentages between 80% and 90% for string representations and between 60% and 80% for data codified as sequences of vectors with respect to their corresponding non-optimized version of the classifier. • Speeding up Nearest Neighbor classification in structural data. • Proposing different bounds for string and consecutive vectors edit distances. • Experimentation and analysis for different corpora. • Bounds significantly reduce classification time for both data representations. • Accuracy is maintained with respect to the baseline as no approximation is performed." @default.
• W3091808987 created "2020-10-15" @default.
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• W3091808987 date "2020-12-01" @default.
• W3091808987 modified "2023-09-24" @default.
• W3091808987 title "Bounding Edit Distance for similarity-based sequence classification on Structural Pattern Recognition" @default.
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• W3091808987 doi "https://doi.org/10.1016/j.asoc.2020.106778" @default.
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