• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.459-483
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• 1994

The purpose of the study is to design and implement a prototype expert system for library classification in the literature field of the DDC 20. The system was largely consisted of a knowledge base, an inference engine, a knowledge acquisition facility, an explanation facility and an user interface facility. The knowledge base was represented by inference rules and frames. The name file for authors and titles was designed separately. The forward chaining technique was chosen for the inference engine and the menu-driven dialog technique was also taken for the user interface. The conclusions of the study can be summarized as follows: 1) The difficulty of document classification work is due to the complex and stringent classification rules. Such problems can be considerably alleviated by using the present system. 2) Even the novice with a knowledge about the DDC 20 can easily access the system. And also librarian other than the professional classifier can easily be accustomed to the classification work. 3) The system can be used as an online classification scheme. 4) By adding any local language other than English or Hangeul on the menu screen, the language problem relating classification can be overcome. 5) The system can be employed as the intensification tool for the education of classification as well as library automation.

• Korean Journal of Cognitive Science
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• v.6 no.3
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• pp.25-40
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• 1995

This paper proposes a generating method of fuzzy rules by genetic and descent method (GAGDM),and its applied to classification problems.The number of inference rules and the shapes of membership function in the antecedent part are detemined by applying the genetic algorithm,and the real numbers of the consequent parts are derived by using the descent method.The aim of the proposed method is to generation a minmun set of fuzzy rules that can correctly classify all training patterns,and fiteness function of GA defined by the aim of th proposed method.Finally,in order to demonstrate the effectiveness of the present method,simulation results are shown.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of Ocean Engineering and Technology
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• v.28 no.1
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• pp.77-84
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• 2014

Recently, CFRP composites have often been used as the materials for lightweight pleasure yacht hulls. Because CFRP composites not only make the hull light but also have good physical characteristics, in the leading countries of the marine industries, CFRP yachts are being sold at a higher price. The design and construction of FRP composite yachts, including those made of CFRP, have to follow rules based on ISO 12215, such as the hull structure rules of the international classification societies. On the other hand, there are no rules related to CFRP composites in the Guidance for Recreational Crafts, which was newly revised by the Korean Register of Shipping. In this paper, ISO 12215-5 and Part B, RINA Pleasure Yacht (REGISTRO ITALIANO NAVALE) on the design pressure and scantling of CFRP hull structures are compared and analyzed. Through a comparative study and the application to a cruise yacht design, we try to understand how to design CFRP hull structures using the international standards, ISO 12215, and the rules of the international classification society, RINA.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1739-1748
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• 2008

When we conduct a data mining procedure on sample data sources, several rules are generated. But some rules are redundant or logically disjoint and therefore they can be removed. We suggest a new rule minimization algorithm inspired from logic synthesis to improve comprehensibility and eliminate redundant rules. The method can merge several relevant rules into one based on data mining and logic synthesis without high loss of accuracy. In case of two or more rules are candidates to be merged, we merge the rules with the attribute having the lowest information gain. To show the proposed method could be a reasonable solution, we applied the proposed approach to a problem domain constructing user preferred ontology in anti-spam systems.

• Bulletin of the Society of Naval Architects of Korea
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• v.1 no.1
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• pp.3-8
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• 1964

The classification society rules [1], [2], [3], [4] on the thicknesses of the bulkhead platings are represented graphically. Fig. 1 shows that every rule value for the thickness of the ordinary watertight bulkhead plating is than theoretical value calculated by Timoshenko's formula (1) for ${\sigma}_y=35,000$ psi and ｋ=0.4998. When a flooding due to damage occurred, however, the stiffened edges would yield and clamped edges would change to plastic hinges. In such case, the maximum bending stress at the clamped edges are reduced considerably. The rule values, therefore, are supposed to be acceptable in spite of their insufficient scantlings compared with the values calculated by (2). In the ordinary watertight bulkhead platings A.B.S. Rules give the largest scantlings of all. Fig. 2 shows that A.B.S. Rules and K.R.-N.K. Rules give larger values than the calculated by (2) for deep tank bulkhead platings. But Lloyd Rules give the smaller thickness than equation (2). The special requirements for corrosion bulkhead platings are not studied here.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.723-734
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• 2004

In this paper, we present an automated Web page classification system based upon ontology. As a first step, to identify the representative terms given a set of classes, we compute the product of term frequency and document frequency. Secondly, the information gain of each term prioritizes it based on the possibility of classification. We compile a pair of the terms selected and a web page classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 78 terms out of 240 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 83.52%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.594-601
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• 2006

Coding of occupational and industrial codes is a major operation in census survey of Korean statistics bureau. The coding process has been done manually. Such manual work is very labor and cost intensive and it usually causes inconsistent results. This paper proposes an automatic coding system based on example-based learning. The system converts natural language input into corresponding numeric codes using code generation system trained by example-based teaming after applying manually built rules. As experimental results performed with training data consisted of 400,000 records and 260 manual rules, the proposed system showed about 76.69% and 99.68% accuracy for occupational code classification and industrial code classification, respectively.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.11-25
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• 2001

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.