• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.672-674
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• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.134-146
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• 2009

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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.202-209
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• 2002

This paper deals with simplification of classification rules for data mining and rule bases for control systems. Datamining that extracts useful information from such a large amount of data is one of important issues. There are various ways in classification methodologies for data mining such as the decision trees and neural networks, but the result should be explicit and understandable and the classification rules be short and clear. The rough sets theory is an effective technique in extracting knowledge from incomplete and inconsistent data and provides a good solution for classification and approximation by using various attributes effectively This paper investigates granularity of knowledge for reasoning of uncertain concopts by using rough set approximations and uses a hierarchical classification structure that is more effective technique for classification by applying core to upper level. The proposed classification methodology makes analysis of an information system eary and generates minimal classification rules.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.767-772
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• 2005

The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.7
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• pp.1193-1205
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• 1994

This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.64-74
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• 1997

In many decision making systems, rule-based approaches are used to solve complex problems in the areas of pattern analysis and computer vision. In this paper, we present methods for generating fuzzy IF-THEN rules automatically from training data for pattern classification and high-level computer vision. The rules are generated by construction minimal approximate fuzzy aggregation networks and then training the networks using gradient descent methods. The training data that represent features are treated as linguistic variables that appear in the antecedent clauses of the rules. Methods to generate the corresponding linguistic labels(values) and their membership functions are presented. In addition, an inference procedure is employed to deduce conclusions from information presented to our rule-base. Two experimental results involving synthetic and real are given.