Development of highly-scalable ILP systems
| Project/Area Number |
14580430
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo University of Science |
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Principal Investigator |
OHWADA Hayato TOKYO UNIVERSITY OF SCIENCE, FACULTY OF SCIENCE AND TECHNOLOGY, PROFESSOR, 理工学部, 教授 (30203954)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2004: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2003: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2002: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Inductive Logic Programming / Abductive Logic Programming / Part-of-speech(POS) tagging / Predicting Misclassification / Genetic Algorithm / Symbolic Evolution / 誤分類修正 |
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| Research Abstract |
(1)The method is based on symbiotic evolution, a variant of genetic algorithm(GA), for improving the predictive accuracy in classifying unknown example. We postulate that the diversity of the results in GA increases the fitness to unknown data. We have developed an ILP system called ILP/SE, which uses symbiotic evolution for the hypothesis search task and uses the learning algorithm of Progol for the other task. ILP/SE judges the class of unknown data by majority using multiple hypothesises obtained in repeated execution. Experiments were conducted to show the performance of ILP/SE using the mutagenesis dataset. (2)We propose a method to choose facts which should be examined for inducing a more accurate hypothesis. The proposed method uses abduction to choose the facts and then adds the results of examinations to background knowledge. We call this method active background-knowledge selection, since it is analogous to active data selection in data mining. Finally, we show the result of an empirical experiment and discuss the effectiveness of our method. (3)The purposes of our research are using existing classification rule which is known to have achievement and explanation power, improving the classification accuracy, and discovering knowledge which helps us for modifying the old knowledge-based classification rule. We firstly predict misclassifications of a given classifier. If a result of the classification rule is predicted to be correct, we accept it. If it is predicted to be misclassification, we choose a new class label using a new classification rule acquired by ILP. We apply this method to Part-of-Speech(POS) tagging in English sentences, which is one of the most successful field for ILP applications.
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Report
(4 results)
Research Products
(13 results)
