Tree edit distance for hierarchical data compatible with HMIL paradigm
B\v{r}etislav \v{S}op\'ik, Tom\'a\v{s} Stren\'a\v{c}ik
TL;DR
This paper introduces a new edit distance measure for hierarchical data structures, compatible with the hierarchical multi-instance learning paradigm, and proves its analytical properties.
Contribution
It defines a novel edit distance for hierarchical data compatible with HMIL, addressing a gap in similarity measures for such data structures.
Findings
Proves the correctness of the defined distance measure.
Applicable to JSON-like hierarchical datasets.
Enhances similarity analysis in hierarchical multi-instance learning.
Abstract
We define edit distance for hierarchically structured data compatible with the hierarchical multi-instance learning paradigm. Example of such data is dataset represented in JSON format where inner Array objects are interpreted as unordered bags of elements. We prove correct analytical properties of the defined distance.
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Time Series Analysis and Forecasting · Neural Networks and Applications