| 講演名 | 2004/11/29 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) , |
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| 抄録(英) | Inductive Logic Programming (ILP) is a combination of inductive learning and first-order logic aiming to learn first-order hypotheses from training examples. ILP has a serious bottleneck in an intractably enormous hypothesis search space. This makes existing approaches perform poorly on large-scale real-world datasets. In this research, we propose a technique to make the system handle an enormous search space efficiently by deriving qualitative information into search heuristics. Currently, heuristic functions used in ILP systems are based only on quantitative information, e.g. number of examples covered and length of candidates. We focus on a kind of data consisting of several parts. The approach aims to find hypotheses describing each class by using both individual and relational features of parts. The data can be found in denoting chemical compound structure for Structure-Activity Relationship. Studies (SAR). We apply the proposed method to extract rules describing chemical activity from their structures. The experiments are conducted on a real-world dataset. The results are compared to existing ILP methods using ten-fold cross validation. |
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| 資料番号 | AI2004-46 |
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| 研究会 | AI |
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| 開催期間 | 2004/11/29(から1日開催) |
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| 申込み研究会 | Artificial Intelligence and Knowledge-Based Processing (AI) |
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| 本文の言語 | ENG |
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| タイトル（英） | 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) |
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| 第 1 著者 氏名（和/英） | / CHOLWICH NATTEE |
| 第 1 著者 所属（和/英） | The Institute of Scientific and Industrial Research, Osaka,University |
| 発表年月日 | 2004/11/29 |
| 資料番号 | AI2004-46 |
| 巻番号（vol） | vol.104 |
| 号番号（no） | 487 |
| ページ範囲 | pp.- |
| ページ数 | 6 |
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