The Experts below are selected from a list of 3393 Experts worldwide ranked by ideXlab platform
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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A new method to construct membership functions and generate fuzzy rules from training instances
International journal of information and management sciences, 2020Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many methods have been proposed to deal with the Iris Data classification problem. In this paper, we present a new method to deal with the Iris Data classification problem by constructing membership functions and generating fuzzy rules from training instances based on the correlation coefficient threshold value ζ, the boundary shift value e and the center shift value δ, where ζ ∈ [0,1], e ∈ [0,1] and δ ∈ [0,1]. The proposed method can get a higher average classification accuracy rate than the existing methods.
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A new method to deal with fuzzy classification problems by tuning membership functions for fuzzy classification systems
Journal of The Chinese Institute of Engineers, 2020Co-Authors: Shyiming Chen, Yaode FangAbstract:Abstract This paper presents a new method to construct and tune membership functions and generate fuzzy classification rules from training instances for handling the Iris Data classification problem. First, we find two attributes of the Iris Data from the training instances that are suitable to serve as classification criteria. Then, we construct and tune the membership functions of these two attributes and generate fuzzy classification rules from the training instances. The proposed method generates the same number of fuzzy classification rules as the number of species of the training instances. It generates fewer fuzzy classification rules and can get a higher average classification accuracy rate than the existing methods.
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Generating fuzzy rules from training instances for fuzzy classification systems
Expert Systems With Applications, 2008Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris Data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris Data classification problem based on the attribute threshold value @a, the classification threshold value @b and the level threshold value @c, where @a@?[0,1], @b@?[0,1] and @c@?[0,1]. The proposed method gets a higher average classification accuracy rate than the existing methods.
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generating weighted fuzzy rules from training Data for dealing with the Iris Data classification problem
International Journal of Applied Science and Engineering, 2006Co-Authors: Yungchou Chen, Lihui Wang, Shyiming ChenAbstract:The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training Data to deal with a specific classification problem. In this paper, we present a new method to generate weighted fuzzy rules from training Data to deal with the Iris Data classification problem. First, we convert the training Data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. The proposed weighted fuzzy rules generation method gets a higher average classification accu- racy rate than the existing methods.
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generating weighted fuzzy rules from training instances using genetic algorithms to handle the Iris Data classification problem
Journal of Information Science and Engineering, 2006Co-Authors: Shyiming ChenAbstract:In recent years, many researchers have focused on applying the fuzzy set theory to generate fuzzy rules from training instances to deal with the Iris Data classification problem. In this paper, we propose a new method to automatically generate weighted fuzzy rules from training instances by using genetic algorithms to handle the Iris Data classification problem, where the attributes appearing in the antecedent parts of the generated fuzzy rules have different weights. The proposed method can achieve a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods.
L.i. Kuncheva - One of the best experts on this subject based on the ideXlab platform.
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will the real Iris Data please stand up
IEEE Transactions on Fuzzy Systems, 1999Co-Authors: James C. Bezdek, J.m. Keller, R. Krishnapuram, L.i. KunchevaAbstract:This correspondence points out several published errors in replicates of the well-known Iris Data, which was collected by Anderson (1935) but first published by Fisher (1936).
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Will the real Iris Data please stand up?
IEEE Transactions on Fuzzy Systems, 1999Co-Authors: J.c. Bezdek, J.m. Keller, R. Krishnapuram, L.i. KunchevaAbstract:This correspondence points out several published errors in replicates of the well-known Iris Data, which was collected by Anderson (1935) but first published by Fisher (1936).
Jutta Hämmerle-uhl - One of the best experts on this subject based on the ideXlab platform.
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Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Martin Rieger, Jutta Hämmerle-uhlAbstract:Biometric system security requires cryptographic protection of sample Data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed Iris Data by conducting Iris recognition on the selectively encrypted Data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture Data, to encrypting classical sample Data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.
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ICASSP - Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Martin Rieger, Jutta Hämmerle-uhlAbstract:Biometric system security requires cryptographic protection of sample Data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed Iris Data by conducting Iris recognition on the selectively encrypted Data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture Data, to encrypting classical sample Data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.
Fu-ming Tsai - One of the best experts on this subject based on the ideXlab platform.
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A new method to construct membership functions and generate fuzzy rules from training instances
International journal of information and management sciences, 2020Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many methods have been proposed to deal with the Iris Data classification problem. In this paper, we present a new method to deal with the Iris Data classification problem by constructing membership functions and generating fuzzy rules from training instances based on the correlation coefficient threshold value ζ, the boundary shift value e and the center shift value δ, where ζ ∈ [0,1], e ∈ [0,1] and δ ∈ [0,1]. The proposed method can get a higher average classification accuracy rate than the existing methods.
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Generating fuzzy rules from training instances for fuzzy classification systems
Expert Systems With Applications, 2008Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris Data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris Data classification problem based on the attribute threshold value @a, the classification threshold value @b and the level threshold value @c, where @a@?[0,1], @b@?[0,1] and @c@?[0,1]. The proposed method gets a higher average classification accuracy rate than the existing methods.
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A new approach to construct membership functions and generate fuzzy rules from training instances
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many researchers focused on the research topic of constructing fuzzy classification systems to deal with the Iris Data classification problem. One of the methods to construct fuzzy classification systems is to construct membership functions at first, and then to generate fuzzy rules. We present a new method to construct membership functions and generate fuzzy rules from training instances based on the correlation coefficient threshold value /spl zeta/, the boundary shift value /spl epsiv/ and the center shift value /spl delta/ to deal with the Iris Data classification problem, where /spl zeta/ /spl epsi/ [0, 1], /spl epsiv//spl epsi/ [0, 1] and /spl delta/ /spl epsi/ [0, 1]. The proposed method can get a higher average classification accuracy rate and generates fewer fuzzy rules than the existing methods.
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FUZZ-IEEE - A new approach to construct membership functions and generate fuzzy rules from training instances
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Shyiming Chen, Fu-ming TsaiAbstract:In recent years, many researchers focused on the research topic of constructing fuzzy classification systems to deal with the Iris Data classification problem. One of the methods to construct fuzzy classification systems is to construct membership functions at first, and then to generate fuzzy rules. We present a new method to construct membership functions and generate fuzzy rules from training instances based on the correlation coefficient threshold value /spl zeta/, the boundary shift value /spl epsiv/ and the center shift value /spl delta/ to deal with the Iris Data classification problem, where /spl zeta/ /spl epsi/ [0, 1], /spl epsiv//spl epsi/ [0, 1] and /spl delta/ /spl epsi/ [0, 1]. The proposed method can get a higher average classification accuracy rate and generates fewer fuzzy rules than the existing methods.
Martin Rieger - One of the best experts on this subject based on the ideXlab platform.
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Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Martin Rieger, Jutta Hämmerle-uhlAbstract:Biometric system security requires cryptographic protection of sample Data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed Iris Data by conducting Iris recognition on the selectively encrypted Data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture Data, to encrypting classical sample Data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.
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ICASSP - Selective Jpeg2000 Encryption of Iris Data: Protecting Sample Data vs. Normalised Texture
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Martin Rieger, Jutta Hämmerle-uhlAbstract:Biometric system security requires cryptographic protection of sample Data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed Iris Data by conducting Iris recognition on the selectively encrypted Data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture Data, to encrypting classical sample Data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.
