A Novel Learning Vector Quantization Inference Classifier

One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones.

[1]  Jeen-Shing Wang,et al.  Self-adaptive neuro-fuzzy inference systems for classification applications , 2002, IEEE Trans. Fuzzy Syst..

[2]  Verónica Bolón-Canedo,et al.  A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..

[3]  Wei-Song Lin,et al.  Self-organizing fuzzy control of multi-variable systems using learning vector quantization network , 2001, Fuzzy Sets Syst..

[4]  Nikola Pavesic,et al.  Premature clustering phenomenon and new training algorithms for LVQ , 2003, Pattern Recognit..

[5]  Fu-Lai Chung,et al.  Fuzzy learning vector quantization , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[6]  James C. Bezdek,et al.  Two soft relatives of learning vector quantization , 1995, Neural Networks.

[7]  Hugo Jair Escalante,et al.  Improved Learning Rule for LVQ Based on Granular Computing , 2015, MCPR.

[8]  James M. Keller,et al.  Additive hybrid networks for fuzzy logic , 1994 .

[9]  Ajith Abraham,et al.  Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques , 2001, IWANN.

[10]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[11]  Fu-Lai Chung,et al.  A fuzzy learning model for membership function estimation and pattern classification , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[12]  Y. Hayashi,et al.  Interpretation of nodes in networks for fuzzy logic , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[13]  José M. N. Vieira,et al.  Neuro-Fuzzy Systems: A Survey , 2004 .

[14]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[15]  Masafumi Hagiwara,et al.  Self-growing learning vector quantization with additional learning and rule extraction abilities , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[16]  Sankar K. Pal,et al.  Fuzzy self-organization, inferencing, and rule generation , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[17]  M. Ghalehnoie,et al.  A novel batch training algorithm for learning vector quantization networks using soft-labeled training data and prototypes , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[18]  Teuvo Kohonen,et al.  Improved versions of learning vector quantization , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[19]  Sansanee Auephanwiriyakul,et al.  On Feature Selection and Rule Extraction for High Dimensional Data: A Case of Diffuse Large B-Cell Lymphomas Microarrays Classification , 2015 .

[20]  Thomas Villmann,et al.  Generalized relevance learning vector quantization , 2002, Neural Networks.

[21]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[22]  Sansanee Auephanwiriyakul,et al.  A novel neuro-fuzzy method for linguistic feature selection and rule-based classification , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[23]  Wisnu Jatmiko,et al.  Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system. , 2002, ISA transactions.

[24]  Zhou Li-hua,et al.  A new technique for generalized learning vector quantization algorithm , 2006 .

[25]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[26]  Thomas Villmann,et al.  Kernelized vector quantization in gradient-descent learning , 2015, Neurocomputing.

[27]  Miin-Shen Yang,et al.  A fuzzy-soft learning vector quantization , 2003, Neurocomputing.

[28]  Wlodzislaw Duch,et al.  Improving Accuracy of LVQ Algorithm by Instance Weighting , 2010, ICANN.

[29]  Nikhil R. Pal,et al.  A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification , 2004, IEEE Transactions on Neural Networks.

[30]  Rudolf Kruse,et al.  Generating classification rules with the neuro-fuzzy system NEFCLASS , 1996, Proceedings of North American Fuzzy Information Processing.

[31]  Krzysztof Grabczewski,et al.  Extraction of crisp logical rules using constructive constrained backpropagation networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[32]  Nicolaos B. Karayiannis,et al.  Fuzzy algorithms for learning vector quantization , 1996, IEEE Trans. Neural Networks.

[33]  Qiangfu Zhao,et al.  Dual Weight Learning Vector Quantization , 2008, 2008 9th International Conference on Signal Processing.

[34]  Chuen-Tsai Sun,et al.  A neuro-fuzzy classifier and its applications , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[35]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.