人工知能学会全国大会論文集
Online ISSN : 2758-7347
36th (2022)
セッションID: 2S6-IS-3d-01
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A study of sequence matching method considering data transition
*Masashi KAMURAHiroyuki KASAI
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会議録・要旨集 フリー

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In this paper, we focus on the problem of measuring the distance between sequences whose order has some meaning. The existing method, Order-Preserving Wasserstein distance (OPW), has a problem that it does not take into account the similarity and neighbor relationship of the elements too much. In this paper, we propose a method that considers the neighbor relationship between elements by using the transitions of elements in addition to OPW matching. From numerical evaluation experiments, we show that the proposed method improves the classification accuracy for some data sets.

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© 2022 The Japanese Society for Artificial Intelligence
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