A Rough Set based Fuzzy Inference System for Mining Temporal Medical Databases

Keerthika, U.

doi:10.5121/ijsc.2012.3304

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…This showed that for decision attributes, these four attributes were sufficient to maintain the classification ability of the information system. By sorting the reduced decision table, the rules of precision >0.75 and coverage >0.05 were selected [30]. Finally, the typical diagnostic rule table is shown in Table 4.…”

Section: Classification Rule Extractionmentioning

confidence: 99%

Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology

Liang

et al. 2017

Applied Sciences

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Abstract:To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount of computation to achieve information dimension compression and knowledge system simplification. However, before this reduction, data must be discretized, and this process causes some degree of information loss. Therefore, to maintain the integrity of the information, we used the improved FCM to make attributes fuzzy instead of discrete before continuing with attribute reduction, and thus, the implicit knowledge and decision rules were more accurate. Our algorithm overcame the defects of the traditional FCM algorithm, which is sensitive to outliers and easily falls into local optima. Our experimental results show that the proposed method improved recognition efficiency without degrading recognition accuracy, which was as high as 97.5%. Furthermore, the meibomian gland morphology was diagnosed efficiently, and thus this method can provide practical application values for the recognition of meibomian gland morphology.

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Section: Classification Rule Extractionmentioning

confidence: 99%

Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology

Liang

et al. 2017

Applied Sciences

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“…The membership function is determined with the expert concern. Accordingly, the rule set will be generated and the decision making will be performed [11] [12]. The fuzzy based interference system is a rule based classifier in which temporal algebra is used to generate the fuzzy rules.…”

Section: Fuzzy Rule Based Decision Makingmentioning

confidence: 99%

Study and Analysis of Predictive Data Mining Approaches for Clinical Dataset

Mittal¹,

Gill²

2013

IJCA

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Data Mining is an assortment of effective tool set to perform the statistical analysis on an immense dataset and to retrieve the valuable information from the dataset. In this work we have carried out an analytical survey on predictive data mining approaches on clinical dataset. The clinical dataset processing is one of the effective and most sensitive area which is studied under an expert environment. The present paper discusses KDD, data mining with reference to clinical expert system analysis, different applications and the approaches that can be used for the predictive data mining in same area. The scope of this paper is confined to the prediction of a person disease, based on symptoms dataset. The strength of data mining approaches in diverse clinical applications is also analyzed.

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Research on Application of Quantum Data Mining Algorithm in Image Character Recognition

Han

Chen

2022

2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)

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A Rough Set based Fuzzy Inference System for Mining Temporal Medical Databases

Abstract: The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to

Cited by 4 publications

References 14 publications

Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology

Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology

Study and Analysis of Predictive Data Mining Approaches for Clinical Dataset

Research on Application of Quantum Data Mining Algorithm in Image Character Recognition

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