Robust fuzzy relational classifier incorporating the soft class labels

Cai, Weiling; Chen, Songcan; Zhang, Daoqiang

doi:10.1016/j.patrec.2007.07.013

Cited by 25 publications

(20 citation statements)

References 23 publications

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“…Lately, in our previous work, we have presented Robust FRC (RFRC) [20] with the aim of enhancing the robustness of FRC. According to the two-step training way of FRC, its robustness is improved from the following two sources: first, use the robust Kernelized FCM (KFCM) [23] to replace FCM; second, employ the soft class label motivated by the fuzzy k-nearest-neighbor [24] to replace the hard class label.…”

Section: Clustering Criterion Dependent On Clustering Centersmentioning

confidence: 99%

“…E.g., by utilizing the class information to guide the clustering process, some supervised clustering [5][6][7] or semi-supervised clustering algorithms [8][9][10] Generally, clustering and classification learnings are usually formulated by different models or criteria, hence it is relatively difficult to cast both into a single framework. To fuse the advantages of both learners together, many existing algorithms [11][12][13][14][15][16][17][18][19][20] handle the clustering learning and classification learning in a sequential or independent manner. As illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Cai

Chen

Zhang

2010

IEEE Trans. Neural Netw.

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Traditional pattern recognition involves two tasks: clustering learning and classification learning.Clustering result can enhance the generalization ability of classification learning, while the class information can improve the accuracy of clustering learning. Hence, both learning methods can complement each other. To fuse the advantages of both learning methods together, many existing algorithms have been developed in a sequential fusing way by first optimizing the clustering criterion and then the classification criterion associated with the obtained clustering results. However, such kind of algorithms naturally fails to achieve the simultaneous optimality for two criteria, and thus have to sacrifice either the clustering performance or the classification performance. To overcome that problem, in this paper, we present a multi-objective simultaneous learning framework (named MSCC) for both clustering and classification learning. MSCC utilizes multiple objective functions to formulate the clustering and classification problems, respectively, and more importantly it employs the Bayesian theory to make these functions all only dependent on a set of the same parameters, i.e., clustering centers which play a role of the bridge connecting the clustering and classification learning. By simultaneously optimizing the clustering centers embedded in these functions, not only the effective clustering performance but also the promising classification performance can be simultaneously attained. Furthermore, from the multiple Pareto-optimality solutions obtained in MSCC, we can get an interesting observation that there is complementarity to great extent between clustering and classification learning processes. Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC.

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Section: Clustering Criterion Dependent On Clustering Centersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Cai

Chen

Zhang

2010

IEEE Trans. Neural Netw.

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“…Recently, some fuzzy relation based methods are proposed to bridge clustering and classification [8][9], which also belong to the first category. Setnes et al [8] proposed FRC to represent a transparent alternative to conventional black-box techniques such as neural networks.…”

Section: Fig 1 a Taxonomy Of The Classifier Design When The Class Inmentioning

confidence: 99%

“…Setnes et al [8] proposed FRC to represent a transparent alternative to conventional black-box techniques such as neural networks. To enhance FRC's robustness, in one of our previous works, we developed RFRC [9] by replacing FCM and hard class labels with Kernelized FCM (KFCM) [10,11] and soft labels, respectively. The training of both algorithms includes two steps.…”

Section: Fig 1 a Taxonomy Of The Classifier Design When The Class Inmentioning

confidence: 99%

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A simultaneous learning framework for clustering and classification

Cai

Chen

Zhang

2009

Pattern Recognition

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Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining

Deng¹,

Mabu²,

Hirasawa³

2013

IEEJ Transactions Elec Engng

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Traditional principal component analysis (PCA) based face recognition algorithms have a low recognition accuracy due to the influence of noise and illumination changes. This paper proposes a robust, intelligent PCA‐based face recognition framework in the complicated illumination database when using multiple training images per person (MTIP‐CID). There are mainly two improvements in the proposed method. One is that a face‐recognition‐oriented genetic‐based clustering algorithm is introduced to reduce the influence of a large number of classes on the classification accuracy in the MTIP‐CID. The other is that a classifier based on fuzzy class association rules (FCARs) is applied to mine the inherent relationships between eigenfaces and to improve the robustness of PCA‐based face recognition in noisy environments. Experimental results on the extended Yale‐B database demonstrate that the proposed framework performs better and is more robust against noise compared with other traditional face recognition algorithms, i.e. linear discriminant analysis (LDA) and local binary patterns (LBPs). © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Robust fuzzy relational classifier incorporating the soft class labels

Cited by 25 publications

References 23 publications

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

A Multiobjective Simultaneous Learning Framework for Clustering and Classification

A simultaneous learning framework for clustering and classification

Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining

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