Rough set approach to knowledge-based decision support

Pawlak, Zdzisław

doi:10.1016/s0377-2217(96)00382-7

Cited by 582 publications

(276 citation statements)

References 16 publications

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“…Before detecting the masses, we enhance the ROIs using the rough set (RS) [22] theory considering the following: (1) In ROIs, the gray value between the lesion and gland tissue is sometimes similar, and it is hard to find a suitable threshold by gray transform method; (2) the gray level is different between the mass and its surroundings, by augmenting the edge gradient, we can reserve the object information; and (3) the RS method can deal with the vagueness and uncertainty.…”

Section: Preprocessing and Segmentationmentioning

confidence: 99%

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

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Section: Preprocessing and Segmentationmentioning

confidence: 99%

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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“…러프집합은 Pawlak에 의해 제안된 것으로 불확실성 (uncertainty), 모호함 (vagueness), 부정확성 (imprecision)을 가지고 있는 데이터집합에서 일관성을 가지는 규칙을 찾아내기 위한 수치해석적 접근 방법이다 (Pawlak, 1997). 러프집합 이론은 모든 개체들은 항상 집합이 형성될 수 있다고 가정하고 일 정한 정보들을 바탕으로 개체들의 동질성 관계 (indiscernibility relationship)을 찾아내는 것이 목적이 다.…”

Section: 러프집합 이론unclassified

Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market

Chung¹,

Oh²

2014

Journal of the Korean Data and Information Science Society

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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.

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“…The data sets that support either policy success or failure are counted to establish the probability of success and failure. This procedure is similar to that used to establish the strength of a decision rule by counting the number of data sets supporting a particular rule [34]. Fig.…”

Section: B Analysis Of the Impact Of Policy Instrumentsmentioning

confidence: 99%

Adaptive Policy Design to Reduce Carbon Emissions: A System-of-Systems Perspective

Agusdinata

Dittmar

2009

IEEE Systems Journal

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Abstract-This paper presents an adaptive policy design approach based on a system-of-systems (SoS) perspective. Using a case of carbon emissions reduction in the residential sector, the SoS perspective is used as a way to structure the policy issue into interdependent relevant systems. This representation of the system provides a framework to test a large number of hypotheses about the evolution of the system's performance using computational experiments. In particular, in a situation where the realized emission level misses the intermediate target, policies can be adapted to meet the policy target. Our approach shows the different policy designs that decision-makers can envision to influence the overall system performance.Index Terms-Adaptive policy design, carbon emission reduction, household emissions, system-of-systems.

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Rough set approach to knowledge-based decision support

Cited by 582 publications

References 16 publications

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market

Adaptive Policy Design to Reduce Carbon Emissions: A System-of-Systems Perspective

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