A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition

Melín, Patricia; Castillo, Oscar

doi:10.1016/j.asoc.2014.04.017

Cited by 232 publications

(74 citation statements)

References 21 publications

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“…Moreover Vella and Ng (2014b) showed the increased stability of ANFIS in terms of risk-adjusted performance when compared to ANN alone. Recently, type-2 (T2) fuzzy logic have gained significant academic attention (see review in Melin and Castillo, 2014) and as of today it remains a primary area of research in the fuzzy logic domain . To our best knowledge, the use of higher order fuzzy logic systems (FLSs) in a high frequency trading environment has not been addressed in the literature before.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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Section: A C C E P T E D Mmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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“…According to Tan et al [7], a cluster with more scattered data are considered more uncertain, therefore a wider FOU should be used in MF antecedent in the class. The paper used total euclidean distance of data points to its cluster (dj) then normalized to determine FOU.…”

Section: ) Designing Footprint Of Uncertainty (Fou)mentioning

confidence: 99%

“…T2 FS is set to T1 FS-shaped membership functions. Fuzzy logic with a higher order (air-order 2) has in recent years become popular applied in the case of pattern recognition, classification and clustering [7]. Interval type-2 fuzzy sets (IT2 FS) is a special type of T2 FS, FS T2 is the type most widely used due to a lower computational cost than the original FS T2.…”

Section: Introductionmentioning

confidence: 99%

Interval type-2 fuzzy logic system for diagnosis coronary artery disease

Sajiah

Setiawan

Wahyunggoro

2016

CST

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Coronary artery disease (CAD) is a disease that has been the deadliest disease in Indonesia. The ratio of cardiologists over potential patients is not appropriate either. Intelligent system which can help doctors or patients for cheap and efficient diagnosing CAD is needed. M edical record data, acquisition of cardiologist knowledge and computing technology can be utilized for developing fuzzy logic based intelligent system. Type-1 fuzzy logic system (T1 FLS) has been widely used in various fields. T1 FS has limitation in representing and modelling uncertainty and minimize the impact. Whereas, type-2 fuzzy set (T2 FS) was also introduced as fuzzy set that can model uncertainty more sophisticated. T2 FLS does have a higher degree of freedom when modeling uncertainty but it is quite difficult to make the membership function. An interval T2 FS is a T2 FS in which the membership grade on third dimension is the same everywhere so it is simpler than T2 FS. This paper aims to clarify the better capability of IT2 FLS over T1 FLS on the development of CAD diagnosis system. Rules and membership function were formulated with the help of fuzzy c-means. This study illustrated the causes of CAD risk factors, fuzzification, type reduction and defuzzification. The resulted system was tested with percentage split method (50%-50%) to produce training data and testing data. This test is performed ten times with random seed to separate the data set. The resulted system generates an average of 73.78% accuracy, 71.94% sensitivity and 76.52% specificity.

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“…The type-2 fuzzy set has a secondary membership to define the possibilities of uncertainties in primary membership. Thus the performance of handling uncertainties of type-2 fuzzy set is enhanced [14][15][16]. Another similar technique, PFCM [13], proposed interpreted clustering as a possibilistic partition.…”

Section: Introductionmentioning

confidence: 99%

An Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm with Application for MR Image Segmentation

Chen¹,

Li²,

Li³

2016

IJSIP

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A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition

Cited by 232 publications

References 21 publications

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Interval type-2 fuzzy logic system for diagnosis coronary artery disease

An Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm with Application for MR Image Segmentation

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