• Title/Summary/Keyword: Rule Extraction

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Rule extraction from trained neural network using NofM algorithm with improved clustering step (개선된 군집화 단계의 NofM 알고리즘을 이용한 훈련된 신경망으로부터의 규칙추출)

  • Lee, Han-Yul;Ra, Jong-Hei;Kim, Moon-Hyun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2001.10a
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    • pp.581-584
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    • 2001
  • 신경망이 만들어내는 출력에 대한 정보는 수치적으로 분산되어 신경망에 저장되므로, 인간이 직접 해석하기가 힘들다. 본 논문에서는 LRE(link rule extraction)기법인 NofM 알고리즘의 6단계 중에서 초기 단계인 가중치 군집화 단계를 개선하여 추출되는 규칙들의 전제부에 들어가는 규칙 조건들의 수를 조절함으로써, 추출된 규칙이 입력 특성에 대한 정보를 과잉 일반화하거나, 과잉 구체화하는 것을 피할 수 있음을 실험을 통해 보였다. 일반적으로 NofM 알고리즘에서 가중치들을 군집화한 때는 Join 알고리즘을 사용하는데, 본 논문에서는 Join 알고리즘의 Join condition을 0.05부터 0.25까지 0.05씩 점진적으로 확대하여 클러스터링을 하여줌으로써 신경망의 출력에 중요한 역할을 하는 가중치들을 효과적으로 군집화함을 보였다.

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GUI-based HTML2XML Wrapperusing Inductive Reasoning (학습 추론을 이용한 GUI 기반의 HTML2XML 래퍼)

  • Jang, Mun-Seong;Jeong, Jae-Mok;Choe, Il-Hwan;Kim, Hyeong-Ju
    • Journal of KIISE:Databases
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    • v.29 no.4
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    • pp.311-320
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    • 2002
  • The 'wrapper' is a module that extracts and processes information from the specified data source by the pre-composed extraction rule. 'HTML Wrapper for XML' extracts information from the web source as the form of XML document. Since composing the extraction rule is a repetitious and tedious job, it should be done as easy and fast as possible. This paper presents the method to minimize the composing job, which integrates GUI based training and scripting.

Extraction of Hierarchical Decision Rules from Clinical Databases using Rough Sets

  • Tsumoto, Shusaku
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.336-342
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    • 2001
  • One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts decision processes.

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A rule-based recognition system for korean spoken place names

  • Choi, Won-Kyu;Lee, Fi-Hyol;Akizuki, Kageo
    • 제어로봇시스템학회:학술대회논문집
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    • 1989.10a
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    • pp.431-436
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    • 1989
  • A rule-based recognition system for Korean spoken place names using anti-formants which is analyzed by ARMA model is presented. The recognition system is composed of three parts; the extraction, the recognition and the recognition support. As a result of experiment, the recognition rates of city place names was 90.9%.

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An Incremental Rule Extraction Algorithm Based on Recursive Partition Averaging (재귀적 분할 평균에 기반한 점진적 규칙 추출 알고리즘)

  • Han, Jin-Chul;Kim, Sang-Kwi;Yoon, Chung-Hwa
    • Journal of KIISE:Software and Applications
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    • v.34 no.1
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    • pp.11-17
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    • 2007
  • One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it cannot explain how the classification result is obtained. In order to overcome this problem, we propose an incremental teaming algorithm based on RPA (Recursive Partition Averaging) to extract IF-THEN rules that describe regularities inherent in training patterns. But rules generated by RPA eventually show an overfitting phenomenon, because they depend too strongly on the details of given training patterns. Also RPA produces more number of rules than necessary, due to over-partitioning of the pattern space. Consequently, we present the IREA (Incremental Rule Extraction Algorithm) that overcomes overfitting problem by removing useless conditions from rules and reduces the number of rules at the same time. We verify the performance of proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

Rule Acquisition Using Ontology Based on Graph Search (그래프 탐색을 이용한 웹으로부터의 온톨로지 기반 규칙습득)

  • Park, Sangun;Lee, Jae Kyu;Kang, Juyoung
    • Journal of Intelligence and Information Systems
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    • v.12 no.3
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    • pp.95-110
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    • 2006
  • To enhance the rule-based reasoning capability of Semantic Web, the XRML (eXtensible Rule Markup Language) approach embraces the meta-information necessary for the extraction of explicit rules from Web pages and its maintenance. To effectuate the automatic identification of rules from unstructured texts, this research develops a framework of using rule ontology. The ontology can be acquired from a similar site first, and then can be used for multiple sites in the same domain. The procedure of ontology-based rule identification is regarded as a graph search problem with incomplete nodes, and an A* algorithm is devised to solve the problem. The procedure is demonstrated with the domain of shipping rates and return policy comparison portal, which needs rule based reasoning capability to answer the customer's inquiries. An example ontology is created from Amazon.com, and is applied to the many online retailers in the same domain. The experimental result shows a high performance of this approach.

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Simplification of LIDAR Data for Building Extraction Based on Quad-tree Structure

  • Du, Ruoyu;Lee, Hyo Jong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2011.11a
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    • pp.355-356
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    • 2011
  • LiDAR data is very large, which contains an amount of redundant information. The information not only takes up a lot of storage space but also brings much inconvenience to the LIDAR data transmission and application. Therefore, a simplified method was proposed for LiDAR data based on quad-tree structure in this paper. The boundary contour lines of the buildings are displayed as building extraction. Experimental results show that the method is efficient for point's simplification according to the rule of mapping.

Web Document-based Associate Knowledge Extraction Method : Applying to Bioinformatics (웹 도큐먼트 기반 연관 지식 추출 기법 : 생명정보분야에의 적용)

  • 문현정;김교정
    • Journal of Internet Computing and Services
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    • v.2 no.5
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    • pp.9-19
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    • 2001
  • In this paper. we develop associate knowledge extraction method for finding and expanding user preference knowledge automatically from web document database. To reflect user interest or preferences, agent explores and extracts relevant information to central term involving the intent of users from the example documents. To do so, we apply association rule exploration data-mining method to the extraction of the relevant objects in the web documents. Also, to give the weighted-value to the extracted and relevant information, we present associate tag block-based weighting method. We applied to bioinformatics above associate knowledge extraction method to find related keywords.

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Competition Relation Extraction based on Combining Machine Learning and Filtering (기계학습 및 필터링 방법을 결합한 경쟁관계 인식)

  • Lee, ChungHee;Seo, YoungHoon;Kim, HyunKi
    • Journal of KIISE
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    • v.42 no.3
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    • pp.367-378
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    • 2015
  • This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.