• Title/Summary/Keyword: similarity rule

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The Design and Implementation of An Intelligent Neuro-Fuzzy System(INFS) (지능적인 뉴로-퍼지 시스템의 설계 및 구현)

  • 조영임;황종선;손진곤
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.5
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    • pp.149-161
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    • 1994
  • The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

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Development of a New Max-Min Compositional Rule of Inference in Control Systems

  • Cho, Young-Im
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.776-782
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    • 2004
  • Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

UMLS Semantic Network Automatic Clustering Method using Structural Similarity (구조적 유사성을 이용한 UMLS 의미망 군집 방법)

  • 지영신;전혜경;정헌만;이정현
    • Proceedings of the IEEK Conference
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    • 2003.11b
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    • pp.223-226
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    • 2003
  • Because UMLS semantic network is bulky and complex, user hard to understand and has shortcoming that can not express all semantic network on screen. To solve this problem, rules to dismember semantic network efficiently are introduction. but there is shortcoming that this should classifies manually applying rule whenever UMLS semantic network is modified. Suggest automatic clustering method of UMLS semantic network that use genetic algorithm to solve this problem. Proposed method uses Linked semantic relationship between each semantic type and semantic network does clustering by structurally similar semantic type linkages. To estimate the performance of suggested method, we compared it with result of clustering method by rule.

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Identifying Core Robot Technologies by Analyzing Patent Co-classification Information

  • Jeon, Jeonghwan;Suh, Yongyoon;Koh, Jinhwan;Kim, Chulhyun;Lee, Sanghoon
    • Asian Journal of Innovation and Policy
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    • v.8 no.1
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    • pp.73-96
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    • 2019
  • This study suggests a new approach for identifying core robot tech-nologies based on technological cross-impact. Specifically, the approach applies data mining techniques and multi-criteria decision-making methods to the co-classification information of registered patents on the robots. First, a cross-impact matrix is constructed with the confidence values by applying association rule mining (ARM) to the co-classification information of patents. Analytic network process (ANP) is applied to the co-classification frequency matrix for deriving weights of each robot technology. Then, a technique for order performance by similarity to ideal solution (TOPSIS) is employed to the derived cross-impact matrix and weights for identifying core robot technologies from the overall cross-impact perspective. It is expected that the proposed approach could help robot technology managers to formulate strategy and policy for technology planning of robot area.

An Extended Work Architecture for Online Threat Prediction in Tweeter Dataset

  • Sheoran, Savita Kumari;Yadav, Partibha
    • International Journal of Computer Science & Network Security
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    • v.21 no.1
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    • pp.97-106
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    • 2021
  • Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

Interpretability Comparison of Popular Decision Tree Algorithms (대표적인 의사결정나무 알고리즘의 해석력 비교)

  • Hong, Jung-Sik;Hwang, Geun-Seong
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.2
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    • pp.15-23
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    • 2021
  • Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

Similarity Measurement Between Titles and Abstracts Using Bijection Mapping and Phi-Correlation Coefficient

  • John N. Mlyahilu;Jong-Nam Kim
    • Journal of the Institute of Convergence Signal Processing
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    • v.23 no.3
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    • pp.143-149
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    • 2022
  • This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.

A implementation and evaluation of Rule-Based Reverse-Engineering Tool (규칙기반 역공학 도구의 구현 및 평가)

  • Bae Jin Young
    • Journal of the Korea Society of Computer and Information
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    • v.9 no.3
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    • pp.135-141
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    • 2004
  • With the diversified and enlarged softwares, the issue of software maintenance became more complex and difficult and consequently, the cost of software maintenance took up the highest portion in the software life cycle. We design Reverse Engineering Tool for software restructuring environment to object-oriented system. We design Rule - Based Reverse - Engineering using Class Information. We allow the maintainer to use interactive query by using Prolog language. We use similarity formula, which is based on relationship between variables and functions, in class extraction and restructuring method in order to extract most appropriate class. The visibility of the extracted class can be identified automatically. Also, we allow the maintainer to use query by using logical language. So We can help the practical maintenance. Therefore, The purpose of this paper is to suggest reverse engineering tool and evaluation reverse engineering tool.

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Optimal Associative Neighborhood Mining using Representative Attribute (대표 속성을 이용한 최적 연관 이웃 마이닝)

  • Jung Kyung-Yong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.4 s.310
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    • pp.50-57
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    • 2006
  • In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

A Leveling and Similarity Measure using Extended AHP of Fuzzy Term in Information System (정보시스템에서 퍼지용어의 확장된 AHP를 사용한 레벨화와 유사성 측정)

  • Ryu, Kyung-Hyun;Chung, Hwan-Mook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.2
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    • pp.212-217
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    • 2009
  • There are rule-based learning method and statistic based learning method and so on which represent learning method for hierarchy relation between domain term. In this paper, we propose to leveling and similarity measure using the extended AHP of fuzzy term in Information system. In the proposed method, we extract fuzzy term in document and categorize ontology structure about it and level priority of fuzzy term using the extended AHP for specificity of fuzzy term. the extended AHP integrates multiple decision-maker for weighted value and relative importance of fuzzy term. and compute semantic similarity of fuzzy term using min operation of fuzzy set, dice's coefficient and Min+dice's coefficient method. and determine final alternative fuzzy term. after that compare with three similarity measure. we can see the fact that the proposed method is more definite than classification performance of the conventional methods and will apply in Natural language processing field.