• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.281-292
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• 2014

As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.25-33
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• 2009

Recently, the TS fuzzy models that include the linear equations in the consequent part are widely used for time series forecasting, and the prediction performance of them is somewhat dependent on the characteristics of time series such as stationariness. Thus, a new prediction method is suggested in this paper which is especially effective to nonstationary time series prediction. First, data preprocessing is introduced to extract the patterns and regularities of time series well, and then multiple model TS fuzzy predictors are constructed. Next, an appropriate model is chosen for each input data by an adaptive model selection mechanism based on rough sets, and the prediction is going. Finally, the error compensation procedure is added to improve the performance by decreasing the prediction error. Computer simulations are performed on typical cases to verify the effectiveness of the proposed method. It may be very useful for the prediction of time series with uncertainty and/or nonstationariness because it handles and reflects better the characteristics of data.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.387-394
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• 2013

Data mining is the process of discovering hidden, non-trivial patterns in large amounts of data records in order to be used very effectively for analysis and forecasting. Because hundreds of variables give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. Hence cluster analysis is a main task of data mining and is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper system implementation is of great significance, which defines a new definition based on information-theoretic entropy and analyse the analogue behaviors of objects at hand so as to address the measurement of uncertainties in the classification of categorical data. The sources were taken from a survey aimed to identify of job guidance from students in high school pyeongtaek. we show how variable precision information-entropy based rough set can be used to group student in each section. It is proved that the proposed method has the more exact classification than the conventional in attributes more than 10 and that is more effective in job guidance for students.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.460-465
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• 2007

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1153-1166
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• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• Proceedings of the KOR-KST Conference
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• 2007.02a
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• pp.384-390
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• 2007

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.337-339
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• 1996

In this paper, we construct rough relational database model using approximation concepts of rough set. Also, we analyze the relation between objects, attributes and attribute values and, propose the method that can generate flexible retrieval results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.30-38
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• 2006

This paper is used the color space distribution HMMD model presented in MPEG-7 in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the video object segmentation. Here, it is applied the wavelet morphology to remove a small part that is regarded as a noise in image and a part excepting for the face image. Also, it did the optimal composition by the rough set. In this paper, tile proposed video object detection algorithm is confirmed to be superior as detecting the face object exactly than the conventional algorithm by applying those to the different size images.put the of paper here.