区間・領域分割を用いた Regression Tree の構成  [in Japanese] Efficient Construction of Regression Trees with Range and Region Splitting  [in Japanese]

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Abstract

近年データマイニング(data mining)がデータベース研究と人工知能研究の境界分野として注目を集めているが、そのなかの一トピックとしてデータベースからのregression tree構成がある。従来のregression tree構成においては内部ノードを分割する際そのノードに対応するデータセットをある単一属性の値がある定数値より小さいか否かで振り分ける手法(guillotine-cut)がとられてきたが、我々はそれに代わるものとして、データセットを2つの数値属性が張る平面中のある領域に含まれるか否かで振り分ける手法をその効率的な実現法とともに提案し、あわせて実装およぴ実験を行った。

In recent years data mining has attracted many researchers among both artificial intelligence and database communities. Construction of regression trees is a topic of data mining. A regression tree is a rooted binary tree such that each internal node contains a test for splitting tuples into two disjoint classes. The mean of the objective attribute values at the leaf is used as the predicted value of the tuple. To test a numerical attribute, traditional methods use a guillotine-cut splitting that classifies data into those below a given value and others. In this paper, as an alternative of guillotine-cut splitting, we consider a family R of grid-regions in the plane associated with two given numeric attributes. And we propose to use a test that splits data into those that lie inside a region R and those that lie outside. Some experimental results showed that regression trees constructed through our method have higher accuracy than those through guillotine-cut splitting.

Journal

  • IEICE technical report. Artificial intelligence and knowledge-based processing

    IEICE technical report. Artificial intelligence and knowledge-based processing 97(415), 57-62, 1997-12-02

    The Institute of Electronics, Information and Communication Engineers

References:  13

Cited by:  1

Codes

  • NII Article ID (NAID)
    110003186761
  • NII NACSIS-CAT ID (NCID)
    AN10013061
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • Data Source
    CJP  CJPref  NII-ELS 
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