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Journal articles on the topic 'Fuzzy Pattern Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

ISHIBUCHI, Hisao, Ken NOZAKI, and Hideo TANAKA. "Pattern Classification by Fuzzy Rules." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 1 (1993): 74–84. http://dx.doi.org/10.3156/jfuzzy.5.1_74.

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2

Hamilton-Wright, A., D. W. Stashuk, and H. R. Tizhoosh. "Fuzzy Classification Using Pattern Discovery." IEEE Transactions on Fuzzy Systems 15, no. 5 (October 2007): 772–83. http://dx.doi.org/10.1109/tfuzz.2006.889930.

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3

Meher, Saroj K. "Explicit rough–fuzzy pattern classification model." Pattern Recognition Letters 36 (January 2014): 54–61. http://dx.doi.org/10.1016/j.patrec.2013.09.002.

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4

Kulkarni, Arun, and Nikita kulkarni. "Fuzzy Neural Network for Pattern Classification." Procedia Computer Science 167 (2020): 2606–16. http://dx.doi.org/10.1016/j.procs.2020.03.321.

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5

Ray, K. S., and T. K. Dinda. "Pattern classification using fuzzy relational calculus." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 1 (February 2003): 1–16. http://dx.doi.org/10.1109/tsmcb.2002.804361.

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6

Ray, Kumar S. "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450009. http://dx.doi.org/10.1142/s0219467814500090.

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In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one-dimensional fuzzy implication. For the estimation of Ri we use floating point representation of GA. Thus a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non-fuzzy features of a test pattern can be classified. The performance of the proposed scheme is tested on synthetic data. Subsequently, we use the proposed scheme for the vowel classification problem of an Indian language. Finally, a benchmark of performance is established by considering multiplayer perception (MLP), support vector machine (SVM) and the present method. The Abalone, Hosse Colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. This new tool for pattern classification is very effective for classification of patterns under vegue and imprecise environment. It can provide multiple classification under overlapped classes.

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7

PAPAKOSTAS, G. A., Y. S. BOUTALIS, D. E. KOULOURIOTIS, and B. G. MERTZIOS. "FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1461–86. http://dx.doi.org/10.1142/s0218001408006910.

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A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

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8

Nakashima, Tomoharu, Yasuyuki Yokota, Hisao Ishibuchi, Gerald Schaefer, Aleš Drastich, and Michal Závišek. "Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 546–53. http://dx.doi.org/10.20965/jaciii.2007.p0546.

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We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.

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9

Lee, In-K., Chang-S. Son, and Soon-H. Kwon. "Ontology-based Fuzzy Classifier for Pattern Classification." Journal of Korean Institute of Intelligent Systems 18, no. 6 (December 25, 2008): 814–20. http://dx.doi.org/10.5391/jkiis.2008.18.6.814.

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10

Lee, Sang-Bum, Sung-joo Lee, and Mai-Rey Lee. "Selecting Fuzzy Rules for Pattern Classification Systems." International Journal of Fuzzy Logic and Intelligent Systems 2, no. 2 (June 1, 2002): 159–65. http://dx.doi.org/10.5391/ijfis.2002.2.2.159.

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11

Ozols, J., and A. Borisov. "Fuzzy classification based on pattern projections analysis." Pattern Recognition 34, no. 4 (April 2001): 763–81. http://dx.doi.org/10.1016/s0031-3203(00)00029-7.

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12

Salama, M. M. A., and R. Bartnikas. "Fuzzy logic applied to PD pattern classification." IEEE Transactions on Dielectrics and Electrical Insulation 7, no. 1 (2000): 118–23. http://dx.doi.org/10.1109/94.839349.

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13

Senge, Robin, and Eyke Hullermeier. "Fast Fuzzy Pattern Tree Learning for Classification." IEEE Transactions on Fuzzy Systems 23, no. 6 (December 2015): 2024–33. http://dx.doi.org/10.1109/tfuzz.2015.2396078.

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14

Bortolan, G., R. Silipo, and C. Marchesi. "Fuzzy pattern classification and the connectionist approach." Pattern Recognition Letters 17, no. 6 (May 1996): 661–70. http://dx.doi.org/10.1016/0167-8655(96)00031-1.

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15

HU, YI-CHUNG, and FANG-MEI TSENG. "MINING SIMPLIFIED FUZZY IF-THEN RULES FOR PATTERN CLASSIFICATION." International Journal of Information Technology & Decision Making 08, no. 03 (September 2009): 473–89. http://dx.doi.org/10.1142/s021962200900348x.

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A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.

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16

BROUWER, ROELOF K. "A FUZZY RECURRENT ARTIFICIAL NEURAL NETWORK (FRANN) FOR PATTERN CLASSIFICATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 05 (October 2000): 525–38. http://dx.doi.org/10.1142/s021848850000037x.

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This paper proposes a recurrent neural network of fuzzy units, which may be used for approximating a hetero-associative mapping and also for pattern classification. Since classification is concerned with set membership, and objects generally belong to sets to various degrees, a fuzzy network seems a natural for doing classification. In the network proposed here each fuzzy unit defines a fuzzy set. The fuzzy unit in the network determines the degree to which the input vector to the unit lies in that fuzzy set. The fuzzy unit may be compared to a perceptron in which case the input vector is compared to the weighting vector associated with the unit by taking the dot product. The resulting membership value in case of the fuzzy unit is compared to a threshold. Training of a fuzzy unit is based on an algorithm for solving linear inequalities similar to the method used for Ho-Kashyap recording. Training of the whole network is done by training each unit separately. The training algorithm is tested by trying the algorithm out on representations of letters of the alphabet with their noisy versions. The results obtained by the simulation are very promising.

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17

Caballero, Amaury. "The use of fuzzy logic in classification." MATEC Web of Conferences 292 (2019): 04008. http://dx.doi.org/10.1051/matecconf/201929204008.

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When determining the degree of coincidence of any multi-feature obtained information, received in the form of a fuzzy vector, to a pre-established known pattern, two general steps should be followed. The first step is to eliminate the features that have little or no effect to the final results and to maintain only those that will influence the pattern recognition. This step could be defined as the classification process and is imperative for the simplification of the problem. One example of classification that could considerably reduce system costs is when using sensors distributed along an industrial process to manage information at a central location. Several methods could be used for classification, such as statistical methods, rough sets, fuzzy logic or information theory. The second step is to find out the correlation between the received fuzzy vector and the vector defining the known pattern using the previously selected features. For this part, the use of fuzzy logic is extremely convenient. The present work analyzes some of the methods used for classification and pattern recognition based on concrete and practical examples

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18

Kbir, M. Ait, H. Benkirane, K. Maalmi, and R. Benslimane. "Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules." Pattern Recognition Letters 21, no. 6-7 (June 2000): 503–9. http://dx.doi.org/10.1016/s0167-8655(00)00015-5.

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19

Vantas, Konstantinos, and Epaminondas Sidiropoulos. "Intra-Storm Pattern Recognition through Fuzzy Clustering." Hydrology 8, no. 2 (March 25, 2021): 57. http://dx.doi.org/10.3390/hydrology8020057.

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The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

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20

NAKAI, Gaku, Tomoharu NAKASHIMA, and Hisao ISHIBUCHI. "A Fuzzy Ensemble Learning Method for Pattern Classification." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 6 (2003): 671–81. http://dx.doi.org/10.3156/jsoft.15.671.

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21

Dinda, Tapan Kr, Kumar S. Ray, and Mihir Kr Chakraborty. "Fuzzy relational calculus approach to multidimensional pattern classification." Pattern Recognition 32, no. 6 (June 1999): 973–95. http://dx.doi.org/10.1016/s0031-3203(98)00133-2.

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22

ALIMORADI, ARZHANG, SHAHRAM PEZESHK, FARZAD NAEIM, and HICHEM FRIGUI. "FUZZY PATTERN CLASSIFICATION OF STRONG GROUND MOTION RECORDS." Journal of Earthquake Engineering 9, no. 3 (May 1, 2005): 307–32. http://dx.doi.org/10.1080/13632460509350544.

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23

Ray, Kumar S. "Pattern classification using fuzzy relation and genetic algorithm." International Journal of Intelligent Computing and Cybernetics 5, no. 4 (November 23, 2012): 533–65. http://dx.doi.org/10.1108/17563781211282277.

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24

Meher, Saroj K. "Efficient pattern classification model with neuro-fuzzy networks." Soft Computing 21, no. 12 (January 8, 2016): 3317–34. http://dx.doi.org/10.1007/s00500-015-2010-0.

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25

SHUNMUGA VELAYUTHAM, C., SATISH KUMAR, and SANDEEP PAUL. "EVOLVABLE SUBSETHOOD PRODUCT FUZZY NEURAL NETWORK FOR PATTERN CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 957–70. http://dx.doi.org/10.1142/s0218001402002088.

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This paper presents an evolvable version of a novel subsethood product fuzzy neural inference system (ESuPFuNIS). The original SuPFuNIS model20 employs only fuzzy weights, and accepts both numeric and linguistic inputs. All numeric inputs are fuzzified using a feature specific fuzzifier. The model composes fuzzy signals from the input layer with fuzzy weights using a mutual subsethood measure. Rule nodes use a product aggregation operator. Outputs from the network are generated using volume defuzzification. Here we replace the original gradient descent learning procedure with a genetic optimization technique and report considerable improvements in classification accuracy and rule economy on three benchmark problems. Real-coded genetic algorithms (RGA's) have been employed to search for an optimal set of network parameters. We demonstrate the classification capabilities of the network on Ripley's synthetic two class data, Iris data and Forensic glass data. In all the problems considered, the GA based classifier performs better than its gradient descent counterpart in terms of classification accuracy as well as rule economy.

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26

Michalíková, Alžbeta. "Intuitionistic fuzzy negations and their use in image classification." Notes on Intuitionistic Fuzzy Sets 26, no. 3 (October 2020): 22–32. http://dx.doi.org/10.7546/nifs.2020.26.3.22-32.

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In this paper, the problem of classification of images is discussed. Our specific problem is that we need to classify tire images into selected classes. The classes are characterized by some patterns. In the first step images are represented as the vectors. Then the membership and non-membership value to each coordinate of the vector is calculated and the theory of intuitionistic fuzzy sets is used. In [7] the classification of images was performed with respect to the valued of so called Sim function, which was defined as a ratio of distance between pattern data and image data and distance between pattern data and the complement of image data. The complement of image data was obtained by using specific intuitionistic fuzzy negation. In [2] a list of 53 intuitionistic fuzzy negations was presented. We have decided to use some of these negations to improve the results of classification.

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27

Haskell, Richard E., Charles Lee, and Darrin M. Hanna. "Geno-fuzzy classification trees." Pattern Recognition 37, no. 8 (August 2004): 1653–59. http://dx.doi.org/10.1016/j.patcog.2004.01.010.

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28

AL-ALAWI, RAIDA. "PERFORMANCE EVALUATION OF FUZZY SINGLE LAYER WEIGHTLESS NEURAL NETWORK." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 03 (June 2007): 381–93. http://dx.doi.org/10.1142/s021848850700473x.

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The paper evaluates the performance of a neuro-fuzzy pattern classification system based on the weightless neural network architecture. The system utilizes a Single Layer Weightless Neural Network (SLWNN) to extract the features vector that measures the similarity of the input pattern to the different classification groups. In contrast to the traditional crisp Winner-Takes-All (WTA) classification scheme used by SLWNN, our system uses a Fuzzy Inference System (FIS) for classification. The network is trained by a hybrid learning scheme that combines a single pass learning phase for training the SLWNN followed by a supervised learning phase for extracting a set of fuzzy rules suitable to classify the training set. The FIS learns fuzzy rules from the feature vectors generated by the SLWNN for the set of training patterns. The recognition of handwritten numerals is employed as a test-bed to demonstrate the effectiveness of the proposed neuro-fuzzy system. Experimental results show that the performance of the proposed system surpasses the performance of the traditional SLWNN.

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29

Bhatt, Rajen B., and M. Gopal. "FRCT: fuzzy-rough classification trees." Pattern Analysis and Applications 11, no. 1 (August 24, 2007): 73–88. http://dx.doi.org/10.1007/s10044-007-0080-z.

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30

HUNG, CHENG-AN, and SHENG-FUU LIN. "AN INCREMENTAL LEARNING NEURAL NETWORK FOR PATTERN CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (September 1999): 913–28. http://dx.doi.org/10.1142/s0218001499000501.

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A neural network architecture that incorporates a supervised mechanism into a fuzzy adaptive Hamming net (FAHN) is presented. The FAHN constructs hyper-rectangles that represent template weights in an unsupervised learning paradigm. Learning in the FAHN consists of creating and adjusting hyper-rectangles in feature space. By aggregating multiple hyper-rectangles into a single class, we can build a classifier, to be henceforth termed as a supervised fuzzy adaptive Hamming net (SFAHN), that discriminates between nonconvex and even discontinuous classes. The SFAHN can operate at a fast-learning rate in online (incremental) or offline (batch) applications, without becoming unstable. The performance of the SFAHN is tested on the Fisher iris data and on an online character recognition problem.

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31

BROUWER, ROELOF K. "GROWING OF A FUZZY RECURRENT ARTIFICIAL NEURAL NETWORK (FRANN) FOR PATTERN CLASSIFICATION." International Journal of Neural Systems 09, no. 04 (August 1999): 335–50. http://dx.doi.org/10.1142/s0129065799000320.

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This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.

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32

Bodyanskiy, Yevgeniy, Anastasiia Deineko, Irina Pliss, and Olha Chala. "Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task." Information Technology and Management Science 23 (December 15, 2020): 12–16. http://dx.doi.org/10.7250/itms-2020-0002.

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The probabilistic neuro-fuzzy system to solve the image classification-recognition task is proposed. The considered system is a “hybrid” of Specht’s probabilistic neural network and the neuro-fuzzy system of Takagi-Sugeno-Kang. It is designed to solve tasks in case of overlapping classes. Also, it is supposed that the initial data that are fed on the input of the system can be represented in numerical, rank, and nominal (binary) scales. The tuning of the network is implemented with the modified procedure of lazy learning based on the concept “neurons at data points”. Such a learning approach allows substantially reducing the consumption of time and does not require large amounts of training dataset. The proposed system is easy in computational implementation and characterised by a high classification speed, as well as allows processing information both in batch and online mode.

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33

Tan, Shing Chiang, and Chee Peng Lim. "Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification." Journal of Intelligent & Fuzzy Systems 22, no. 2,3 (2011): 57–68. http://dx.doi.org/10.3233/ifs-2011-0476.

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34

Ishibuchi, H., T. Yamamoto, and T. Nakashima. "Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 2 (April 2005): 359–65. http://dx.doi.org/10.1109/tsmcb.2004.842257.

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35

INOUE, Takuya, and Shigeo ABE. "Architecture of Fuzzy Support Vector Machines for Pattern Classification." Transactions of the Institute of Systems, Control and Information Engineers 15, no. 2 (2002): 92–98. http://dx.doi.org/10.5687/iscie.15.92.

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36

Chin-Teng Lin, Chang-Mao Yeh, Sheng-Fu Liang, Jen-Feng Chung, and N. Kumar. "Support-vector-based fuzzy neural network for pattern classification." IEEE Transactions on Fuzzy Systems 14, no. 1 (February 2006): 31–41. http://dx.doi.org/10.1109/tfuzz.2005.861604.

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37

Kassani, Peyman Hosseinzadeh, Andrew Beng Jin Teoh, and Euntai Kim. "Evolutionary-modified fuzzy nearest-neighbor rule for pattern classification." Expert Systems with Applications 88 (December 2017): 258–69. http://dx.doi.org/10.1016/j.eswa.2017.07.013.

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38

Kim, Ho Joon, and Hyun S. Yang. "A fuzzy connectionist expert system for visual pattern classification." Robotics and Computer-Integrated Manufacturing 11, no. 3 (September 1994): 233–44. http://dx.doi.org/10.1016/0736-5845(94)90038-8.

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39

Laxmi, Scindhiya, and Shiv Kumar Gupta. "Intuitionistic Fuzzy Proximal Support Vector Machines for Pattern Classification." Neural Processing Letters 51, no. 3 (March 11, 2020): 2701–35. http://dx.doi.org/10.1007/s11063-020-10222-x.

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40

Ishibuchi, Hisao, Ken Nozaki, and Hideo Tanaka. "Efficient fuzzy partition of pattern space for classification problems." Fuzzy Sets and Systems 59, no. 3 (November 1993): 295–304. http://dx.doi.org/10.1016/0165-0114(93)90474-v.

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41

Pedrycz, W., G. Bortolan, and R. Degani. "Classification of electrocardiographic signals: a fuzzy pattern matching approach." Artificial Intelligence in Medicine 3, no. 4 (August 1991): 211–26. http://dx.doi.org/10.1016/0933-3657(91)90013-2.

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42

Praveena, M. D. Anto, A. Christy, L. Suji Helen, S. Jancy, and D. Usha Nandini. "A Fuzzy based technique for Pattern Recognition & Classification." Journal of Physics: Conference Series 1770, no. 1 (March 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/1770/1/012020.

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43

Wang, Yi Qiang, Rui Jian Huang, Tian Yi Xu, and Ke Hong Tang. "Vehicle Model Recognition Based on Fuzzy Pattern Recognition Method." Advanced Materials Research 383-390 (November 2011): 4799–802. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4799.

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The method based on the theory of Fuzzy Pattern Recognition is divided into three parts. Firstly, use Hough transformation to extract the feature points of vehicles, and use the ratio between two absolute distance of adjacent feature points as the characteristic values of vehicles; secondly, use Fuzzy C-mean Classification to handle feature data of 75 car model, then establish a degree of membership matrix as the sample space; thirdly, consider the classification algorithm based on fuzzy approach degree and the credibility of the vehicle feature to propose a weighted close- degree recognition algorithm. This recognition method has a good effect.

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44

Fatichah, Chastine, Martin Leonard Tangel, Muhammad Rahmat Widyanto, Fangyan Dong, and Kaoru Hirota. "Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 3 (May 20, 2012): 412–19. http://dx.doi.org/10.20965/jaciii.2012.p0412.

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The parameter optimization of local fuzzy patterns based on the fuzzy contrast measure is proposed for extracting white blood cell texture. The proposed method obtains the optimal parameter values of the nucleus and cytoplasm region of white blood cell image and the best accuracy rate of white blood cell classification can therefore be achieved. To evaluate the performance of the proposed method, 100 microscopic white blood cell images and the supervised learning method are used for white blood cell classification. Results show that the average accuracy rate of white blood cell classification using local fuzzy pattern features with optimal parameter values of a nucleus and a cytoplasm region is 4% more accurate than with uniform parameter values and is 5–18% more accurate than other feature extraction methods. White blood cell feature extraction is part of the white blood cell classification in an automatic cancer diagnosis that is being developed. In addition, the proposed method can be used to obtain the optimal parameter of local fuzzy patterns for other types of datasets.

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45

Pedrycz, Witold. "Classification in a fuzzy environment." Pattern Recognition Letters 3, no. 5 (September 1985): 303–8. http://dx.doi.org/10.1016/0167-8655(85)90060-1.

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46

Pizzi, Nick J., and Witold Pedrycz. "Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network." Advances in Fuzzy Systems 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/920920.

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Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase.

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47

Son, Chang-S., Suk-T. Seo, Hwan-M. Chung, and Soon-H. Kwon. "Extraction of Classification Boundary for Fuzzy Partitions and Its Application to Pattern Classification." Journal of Korean institute of intelligent systems 18, no. 5 (October 25, 2008): 685–91. http://dx.doi.org/10.5391/jkiis.2008.18.5.685.

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Jamshidi Khezeli, Yazdan, and Hossein Nezamabadi-pour. "Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/206121.

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This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks.

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Wong, Shen Yuong, Keem Siah Yap, and Xiaochao Li. "A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 361. http://dx.doi.org/10.14419/ijet.v7i4.35.22762.

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Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.

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Cheng, H. D., and Rutvik Desai. "Scene Classification by Fuzzy Local Moments." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (November 1998): 921–38. http://dx.doi.org/10.1142/s0218001498000506.

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The identification of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpose. We present a simple and effective method for gray-level image representation and identification which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The technique does not require translation, scaling or rotation of the image. Furthermore, it is suitable for parallel implementation which is an advantage for real-time applications. The classification capability and robustness of the technique are demonstrated by experiments on scaled, rotated and noisy gray-level images of uppercase and lowercase characters and digits of English alphabet, as well as the images of a set of tools. The proposed approach can handle rotation, scale and translation invariance, noise and fuzziness simultaneously.

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