Partial Rule Weighting Using Single-Layer Perceptron(Artificial Intelligence III)

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概要

• 論文の詳細を見る

• Inductive Logic Programming (ILP) has been widely used in Knowledge Discovery in Databases (KDD). The ordinary ILP systems work in two-class domains, not in multi-class domains. We have proposed the method which is be able to help ILP in multi-class domains by using the partial rules extracted from the ILP's rules combined with weighting algorithm to classify unseen examples. In this paper, we improve the weighting algorithm by using single layer perceptron. The learned weights from the perceptrons and the partial rules are then combined to represent the knowledge extracted from the domains. The accuracy of the proposed method on classification of a real-world data set, dopamine antagonist molecules, shows that our approach can remarkably improve the previous weighting algorithm and the original ILP's rules.

• 一般社団法人情報処理学会の論文
• 2004-12-04

著者

• NUMAO MASAYUKI

  The Institute of Scientific and Industrial Research, Osaka University

• Numao Masayuki

  The Institute Of Scientific And Industrial Research Osaka University

• Sinthupinyo Sukree

  The Institute Of Scientific And Industrial Research Osaka University

• NATTEE Cholwich

  The Institute of Scientific and Industrial Research, Osaka University

• OKADA TAKASHI

  School of Science and Technology, Kwansei Gakuin University

• KIJSIRIKUL BOONSERM

  Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University

• Nattee Cholwich

  The Institute Of Scientific And Industrial Research Osaka University

• Kijsirikul Boonserm

  Department Of Computer Engineering Faculty Of Engineering Chulalongkorn University

• Okada Takashi

  School Of Science And Technology Kwansei Gakuin University

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• SBSOM : Self-Organizing Map for Visualizing Structure in the Time Series of Hot Topics(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
• Visualization of Damage Progress in Solid Oxide Fuel Cells
• Inductive Logic Programming for Multiple-Part Data : Applications on Structure-Activity Relationship Studies
• Inductive Logic Programming for Multiple-Part Data: Applications on Structure-Activity Relationship Studies
• Partial Rule Weighting Using Single-Layer Perceptron(Artificial Intelligence III)
• Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure(Scientific Data Mining)
• Partial Rule Weighting Using Single-Layer Perceptron(Artificial Intelligence III)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
• Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure(Scientific Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
• Partial Rule Weighting Using Single-Layer Perceptron (Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining) -- (Session 13: Artificial Intelligence 3)
• Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure (Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining) -- (Session 9: Scientific Data Mining)
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• Feature Discovery in Temporal Data(Artificial Intelligence III)
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• Preprocessing Planning for Data Mining(Artificial Intelligence I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
• Preprocessing Planning for Data Mining (Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining) -- (Session 3: Artificial Intelligence)
• Feature Discovery in Temporal Data(Artificial Intelligence III)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
• A Category-based Framework of a Self-improving Instructional Planner

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