• Title/Summary/Keyword: Hierarchy tree structure classes

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A Heuristic Metric for Measuring Complexity of Class Inheritance Structures (클래스 상속구조에 대한 경험적 복잡성 척도)

  • Chung, Hong;Kim, Tae-Sik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.328-333
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    • 2002
  • The deeper the hierarchy of a inheritance structure is, the better the reusability of the structure is, but the more difficult the understandability and the maintainability of it is. On the contrary, the shallower the hierarchy is, the worse the abstraction of the inheritance structure is, but the better the understandability and modifiability of it is. Therefore, it is to be desired that a deep hierarchy of a inheritance structure should be split to be shallow for the maintainability of a system. This paper proposed a complexity metric that is based on DIT and NOC of Chidamber and Kemerer, and solved the ambiguity of the metrics of them, which was pointed out by Li. The metric is a simple and heuristic one for measuring the complexity of class inheritance structures by considering the number of ancestor classes and descendant classes and the depth of inheritance hierarchy. This provides a quantitative information for assessing the complexity of a inheritance structure in splitting it.

Improvement in the classification performance of Raman spectra using a hierarchical tree structure (계층적 트리 구조를 이용한 라만스펙트럼 판별 성능 개선)

  • Park, Jun-Kyu;Baek, Sung-June;Seo, Yu-Gyeong;Seo, Sung-Il
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.8
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    • pp.5280-5287
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    • 2014
  • This paper proposes a method in which classes are grouped as a hierarchical tree structure for the effective classification of the Raman spectra. As experimental data, the Raman spectra of 28 chemical compounds were obtained, and pre-treated with noise removal and normalization. The spectra that induced a classification error were grouped into the same class and the hierarchical structure class was composed. Each high and low class was classified using a PCA-MAP method. According to the experimental results, the classification of 100% was achieved with 2.7 features on average when the proposed method was applied. Considering that the same classification rates were achieved with 6 features using the conventional method, the proposed method was found to be much better than the conventional one in terms of the total computational complexity and practical application.

A Semantic Hierarchy of Korean Nouns using the Definitions of Words in a Dictionary (사전 뜻 풀이말에서 구축한 한국어 명사 의미 계층구조)

  • 조평옥;안미정;옥철영;이수동
    • Korean Journal of Cognitive Science
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    • v.10 no.4
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    • pp.1-10
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    • 1999
  • A Semantic Hierarchy of Korean Nouns(SHKN) where Korean nouns are semantically and hierarchically classified is one of the most important things that can provide semantic information concerned with processing the Korean sentences. In this paper. SHKN is constructed in bottom-up method by making use of the definition of a noun in the Korean dictionary. SHKN constructed in this paper is a forest which consist of 43 trees and 2.443 non-terminal nodes and 10.347 terminal nodes. depth of which is 17. The classes of level 1. 2 of SHKN is quite different from the existing structure classified in top-down method. 2 b but the lower classes than level 2 is very objective.

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A Semantic Hierarchy of Korean Nouns using the Definitions of Words in a Dictionary (사전 뜻 풀이말에서 구축한 한국어 명사 의미 계층구조)

  • 조평옥;안미정;옥철영;이수동
    • Korean Journal of Cognitive Science
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    • v.10 no.3
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    • pp.1.1-10
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    • 1999
  • A Semantic Hierarchy of Korean Nouns(SHKN) where Korean nouns are semantically and hierarchically classified is one of the most important things that can provide semantic information concerned with processing the Korean sentences. In this paper. SHKN is constructed in bottom-up method by making use of the definition of a noun in the Korean dictionary. SHKN constructed in this paper is a forest which consist of 43 trees and 2.443 non-terminal nodes and 10.347 terminal nodes. depth of which is 17. The classes of level 1. 2 of SHKN is quite different from the existing structure classified in top-down method. 2 b but the lower classes than level 2 is very objective.

The State Attribute and Grade Influence Structure for the RC Bridge Deck Slabs by Information Entropy (정보 엔트로피에 의한 RC 교량 상판의 상태속성 및 등급 영향 구조 분석)

  • Hwang, Jin-Ha;Park, Jong-Hoi;An, Seoung-Su
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.23 no.1
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    • pp.61-71
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    • 2010
  • The attributes related to the health condition of RC deck slabs are analyzed to help us identify and rate the safety level of the bridges in this study. According to the related reports the state assessment for the outward aspects of bridges is the important and critical part for rating the overall structural safety. In this respect, the careful identification for the various state attributes make the field inspection and structural diagnosis very effective. This study analyzes the influence of the state attributes on evaluation classes and the relationship of them by the inductive reasoning, which raise the understanding and performance for evaluation work, and support the logical approach for the state assessment. ID3 algorithm applied to the case set which is constructed from the field reports indicates the main attributes and the precedence governing the assessment, and derives the decision hierarchy for the state assessment.