Development of highly-scalable ILP systems

开发高度可扩展的 ILP 系统

基本信息

  • 批准号:
    14580430
  • 负责人:
  • 金额:
    $ 2.3万
  • 依托单位:
  • 依托单位国家:
    日本
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
  • 财政年份:
    2002
  • 资助国家:
    日本
  • 起止时间:
    2002 至 2004
  • 项目状态:
    已结题

项目摘要

(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.
(1)该方法基于遗传算法(GA)的一种变体--共生进化,以提高对未知样本分类的预测精度。我们假设GA结果的多样性增加了对未知数据的适应性。我们已经开发了一个ILP系统称为ILP/SE,它使用共生进化的假设搜索任务,并使用Progol的学习算法的其他任务。ILP/SE使用在重复执行中获得的多个假设,通过多数来判断未知数据的类别。进行实验以显示使用诱变数据集的ILP/SE的性能。(2)We提出了一种方法来选择事实,这些事实应该被检查以得出更准确的假设。该方法采用溯因法选择事实,然后将考试结果加入到背景知识中。我们称这种方法为主动背景知识选择,因为它类似于数据挖掘中的主动数据选择。最后,我们展示了一个实证实验的结果,并讨论了我们的方法的有效性。(3)研究的目的是利用已有的分类规则,提高分类精度,并发现有助于我们修改旧的基于知识的分类规则的知识。我们首先预测一个给定的分类器的错误分类。如果预测分类规则的结果是正确的,我们接受它;如果预测分类规则的结果是错误的,我们使用ILP获得的新分类规则选择新的类标签。我们将这种方法应用于英语句子的词性标注,这是ILP应用最成功的领域之一。

项目成果

期刊论文数量(18)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using inductive logic programming for question and answering
使用归纳逻辑编程进行问答
岩崎俊英, 松井藤五郎, 大和田勇人: "Predicting and revising misclassification using ILP"13th International Conference on Inductive Logic Programming Short Presentations. 22-29 (2003)
Toshihide Iwasaki、Togoro Matsui、Hayato Owada:“使用 ILP 预测和修正错误分类”第 13 届国际归纳逻辑编程会议简短演示文稿 22-29 (2003)。
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
  • 通讯作者:
Using Symbiotic Evolution to Improve the Predictive Accuracy for Inductive Logic Programming
Active background-knowledge selection in inductive logic programming
归纳逻辑编程中的主动背景知识选择
Predicting and revising misclassification using ILP
使用 ILP 预测和修正错误分类
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

OHWADA Hayato其他文献

OHWADA Hayato的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('OHWADA Hayato', 18)}}的其他基金

Developing algorithm for estrus detection based on social relationships between cows
开发基于奶牛之间社会关系的发情检测算法
  • 批准号:
    18H03294
  • 财政年份:
    2018
  • 资助金额:
    $ 2.3万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Developing a virtual random screening method for cancer drug discovery
开发癌症药物发现的虚拟随机筛选方法
  • 批准号:
    24500364
  • 财政年份:
    2012
  • 资助金额:
    $ 2.3万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Fold prediction from protein of one-dimensional structure using inductive logic programming
使用归纳逻辑编程从一维结构蛋白质进行折叠预测
  • 批准号:
    18500120
  • 财政年份:
    2006
  • 资助金额:
    $ 2.3万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)

相似海外基金

Semantics and Proof Procedure for Abductive Logic Programming
归纳逻辑编程的语义和证明过程
  • 批准号:
    06452404
  • 财政年份:
    1994
  • 资助金额:
    $ 2.3万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了