• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.9
• /
• pp.49-57
• /
• 1999

The fusion of fuzzy set theory and neural networks technologies have concentrated on applying neural networks to obtain the optimal rule bases of fuzzy logic system. Unfortunately, this is very hard to achieve due to limited learning capabilities of neural networks. To overcome this difficulty, we propose a new approach in which rough set theory and neuro-fuzzy fusion are combined to obtain the optimal rule base from input/output data. Compared with conventional FNN, the proposed algorithm is considerably more realistic because it reduces overlapped data when construction a rule base. This results are applied to the construction of inference rules for controlling the temperature at specified points in a refrigerator.

• Journal of Korean Institute of Industrial Engineers
• /
• v.33 no.2
• /
• pp.183-190
• /
• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• Communications of the Korean Mathematical Society
• /
• v.31 no.3
• /
• pp.451-460
• /
• 2016

The notion of a rough fuzzy filter in a BE-algebra is introduced and some properties of such a filter are investigated. The relations between the upper (lower) rough filters and the upper (lower) approximations of their homomorphism images are discussed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.5
• /
• pp.441-447
• /
• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.12
• /
• pp.1095-1105
• /
• 2013

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.12
• /
• pp.179-185
• /
• 2012

This paper proposes a design of RSIDS(RST and SVM based Intrusion Detection System) using RST(Rough Set Theory) and SVM(Support Vector Machine) algorithm. The RSIDS consists of PrePro(PreProcessing) module, RRG(RST based Rule Generation) module, and SAD(SVM based Attack Detection) module. The PrePro module changes the collected information to the data format of RSIDS. The RRG module analyzes attack data, generates the rules of attacks, extracts attack information from the massive data by using these rules, and transfers the extracted attack information to the SAD module. The SAD module detects the attacks by using it, which the SAD module notifies to a manager. Therefore, compared to the existing SVM, the RSIDS improved average ADR(Attack Detection Ratio) from 77.71% to 85.28%, and reduced average FPR(False Positive ratio) from 13.25% to 9.87%. Thus, the RSIDS is estimated to have been improved, compared to the existing SVM.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2010.02a
• /
• pp.413-413
• /
• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.343-349
• /
• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.965-967
• /
• 2013

The paper proposed the DRCS(Disease Rule Creation Scheme) which generates the disease rules for efficient disease management in Healthcare system. The DRCS uses basically Rough Set Theory and computes support between each attributes and decision attributes. It creates the disease rules that judges disease after it removes the attribute which is the lowest support. Therefore, it reduces the number of disease rules and improves the exactness, compared with C4.5 algorithm.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.2
• /
• pp.113-119
• /
• 2008

The World Wide Web is a dynamic collection of pages that includes a huge number of hyperlinks and huge volumes of usage informations. The resulting growth in online information combined with the almost unstructured web data necessitates the development of powerful web data mining tools. Recently, a number of approaches have been developed for dealing with specific aspects of web usage mining for the purpose of automatically discovering user profiles. We analyze sequence data, such as web-logs, protein sequences, and retail transactions. In our approach, we propose the clustering algorithm for sequence data using rough set theory. We present a simple example and experimental results using a splice dataset and synthetic datasets.