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About this paper
Francesco Spinnato
i-BORF extends BORF, a symbolic classifier counting local patterns, by weighting observations by elapsed time since the previous observation in local averages and normalization. It incorporates sampling intervals without imputing or resampling. On ABF, it reaches macro-F1 0.90, versus 0.33 for BORF+RC with the same Ridge head. This dataset-specific result assumes each value represents the preceding interval; pattern extraction remains index-based.
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