Switching between type-2 fuzzy sets and intuitionistic fuzzy sets: an application in medical diagnosis

Own, Chung-Ming

doi:10.1007/s10489-008-0126-y

Cited by 100 publications

(35 citation statements)

References 24 publications

(41 reference statements)

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“…We have compared different theoretical methods [14][15][16][17][18][19][20][21][22][23][24] embedded in our proposed system for attentive function during mobile learning, especially, NNFneural (Neural network fusion), FNF-neural (Fuzzy-neural network fusion) [50][51][52][53][54][55]. Here we give several comparison results and discussions.…”

Section: Comparison and Discussionmentioning

confidence: 99%

A new approach and system for attentive mobile learning based on seamless migration

Zhang

2010

Appl Intell

140

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Seamless migration is one of pervasive computing applications. The function of seamless mobility is suitable for mobile services such as mobile Web-based learning. In this paper, we propose an approach that supports an attentive mobile learning paradigm. This mobile learning dynamically follows the user from place to place and machine to machine without user's awareness or intervention by active service. This capability can be obtained by componentbased smart system and agent-based migrating mechanism. To demonstrate the approach, the theoretical background of fuzzy-neural network for attentive service will be explained. The proposed fusion decision method is based on fuzzyneural network which can make the input signal data to fuse better. Using online tuning, the fusion processing can be accelerated and the fusion belief degree can be improved. Description of mobile learning task and migrating granularity of the task is suggested. The design of the seamless migration mechanism is introduced. This includes solving several important sub-problems, such as transferring delay, transferring failure, and residual computation dependency. Our implemented system for attentive mobile learning based on

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Section: Comparison and Discussionmentioning

confidence: 99%

A new approach and system for attentive mobile learning based on seamless migration

Zhang

2010

Appl Intell

140

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“…This characteristic of type-2 fuzzy sets makes type-2 fuzzy system especially useful to handle corrupted data processing, noise disturbance and knowledge uncertainty, as well as situations where shapes, positions or other parameters of membership functions are uncertain. Recently, type-2 fuzzy system has been used in many successful applications in various areas where uncertainties occur, such as in decision making [11,35], diagnostic medicine [5,38], signal processing [29,44], traffic forecasting [53], mobile robot control [4], pattern recognition [28,47,52], intelligent control [36,49].…”

Section: Advantages and Application Of Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

Multimed Tools Appl

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In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

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“…They introduce complex fuzzy relation as the operator and realize the fuzzy query through the α-cut method. For interval valued fuzzy sets, type-2 fuzzy sets is employed in [5], which is useful for special fuzzy values. Tahani et al [21] firstly advocated the use of fuzzy sets for querying conventional databases, where imprecise conditions inside queries were taken as fuzzy sets.…”

Section: Fuzzy Queriesmentioning

confidence: 99%

“…However, none of existing ranking query methods is suitable for fuzzy semantics. Fuzzy values have been employed to model and handle with vague or ambiguous information in databases since Zadeh [12] introduced the theory of fuzzy sets [5,24,25]. In [21], Tahani et al firstly advocate the use of fuzzy set theory for querying conventional databases, where fuzzy query conditions are seen as fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Efficient fuzzy ranking queries in uncertain databases

Wang

et al. 2011

Appl Intell

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Recently, uncertain data have received dramatic attention along with technical advances on geographical tracking, sensor network and RFID etc. Also, ranking queries over uncertain data has become a research focus of uncertain data management. With dramatically growing applications of fuzzy set theory, lots of queries involving fuzzy conditions appear nowadays. These fuzzy conditions are widely applied for querying over uncertain data. For instance, in the weather monitoring system, weather data are inherent uncertainty due to some measurement errors. Weather data depicting heavy rain are desired, where "heavy" is ambiguous in the fuzzy query. However, fuzzy queries cannot ensure returning expected results from uncertain databases.In this paper, we study a novel kind of ranking queries, Fuzzy Ranking queries (FRanking queries) which extend the traditional notion of ranking queries. FRanking queries are able to handle fuzzy queries submitted by users and return k results which are the most likely to satisfy fuzzy queries in uncertain databases. Due to fuzzy query conditions, the ranks of tuples cannot be evaluated by existing ranking functions. We propose Fuzzy Ranking Function to calculate tuples' ranks in uncertain databases for both attribute-level and tuple-level uncertainty models. Our ranking function take both the uncertainty and fuzzy semantics into account. FRanking queries are formally defined based on Fuzzy Ranking Function. In the processing of answering FRanking queries, we present a pruning method which safely prunes unnecessary tuples to reduce the search space. To further improve the efficiency, we design an efficient algorithm, namely Incremental Membership Algorithm (IMA) which efficiently answers FRanking queries by evaluating the ranks of incremental tuples under each threshold for the fuzzy set. We demonstrate the effectiveness and efficiency of our methods through the theoretical analysis and experiments with synthetic and real datasets.

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Switching between type-2 fuzzy sets and intuitionistic fuzzy sets: an application in medical diagnosis

Cited by 100 publications

References 24 publications

A new approach and system for attentive mobile learning based on seamless migration

A new approach and system for attentive mobile learning based on seamless migration

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Efficient fuzzy ranking queries in uncertain databases

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