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統計的決定理論に基づく階層構造を利用したマルチラベル分類法について

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Other Title
  • トウケイテキ ケッテイ リロン ニ モトズク カイソウ コウゾウ オ リヨウ シタ マルチラベル ブンルイホウ ニ ツイテ
  • 統計的決定理論に基づく階層構造を利用したマルチラベル分類法について (情報論的学習理論と機械学習)
  • Hierarchical Multi-label Classification on Statistical Decision Theory
  • 情報論的学習理論と機械学習
  • ジョウホウロンテキ ガクシュウ リロン ト キカイ ガクシュウ

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Abstract

This paper considers multi-label classification on statistical decision theory. In Label Power Set format, multi-label classification is equivalent to multi-class classification. However, the number of classes increases exponentially as elements in label set grow in number. Hence in case of many labels, a prohibitive computational cost problem occurs. To avoid this problem, some studies have been done and one of them used hierarchical structure. On the other hand, optimal classification method based on bayes rule has been attracted much attention recently. We apply this optimal classification method based on bayes rule to multi-label classification problem. Moreover, assuming hierarchical structure on labels, we propose efficient classification algorithms which reduce computational cost to linear order on the number of elements in label set. Since optimal classification based on bayes rule differs calculation formula depending loss function, we present algorithms in case of O-1 loss and hamming loss, respectively.

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Details

  • CRID
    1520572359258005760
  • NII Article ID
    110009713028
  • NII Book ID
    AA1123312X
  • ISSN
    09135685
  • NDL BIB ID
    024409270
  • Web Site
    http://id.ndl.go.jp/bib/024409270
  • Text Lang
    ja
  • NDL Source Classification
    • ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • Data Source
    • NDL
    • CiNii Articles

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