Modular pattern classifiers: a brief survey

While solving a complex pattern classification problem, it is often difficult to design a monolithic classifier. One approach is to divide the problem into smaller ones, and solve each subproblem using a simpler classifier. This kind of divide and conquer policy has motivated the researchers to substitute a modular classifier for the single monolithic classifier. The paper reviews the advantages, issues and various techniques available for designing the modular classifiers.

[1]  Sung-Bae Cho,et al.  Combining multiple neural networks by fuzzy integral for robust classification , 1995, IEEE Trans. Syst. Man Cybern..

[2]  Sylvian R. Ray,et al.  Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences , 1998, Intell. Data Anal..

[3]  Sun-Yuan Kung,et al.  Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.

[4]  Pedro M. Domingos Knowledge Discovery Via Multiple Models , 1998, Intell. Data Anal..

[5]  Javier Muguerza,et al.  A modular neural network approach to fault diagnosis , 1996, IEEE Trans. Neural Networks.

[6]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[7]  Fouad Badran,et al.  Multimodular Architecture for Remote Sensing Operations. , 1991, NIPS 1991.

[8]  Xin Yao,et al.  Speciation as automatic categorical modularization , 1997, IEEE Trans. Evol. Comput..

[9]  P. Gader,et al.  Advances in fuzzy integration for pattern recognition , 1994, CVPR 1994.

[10]  James M. Keller,et al.  Training the fuzzy integral , 1996, Int. J. Approx. Reason..

[11]  Sung-Bae Cho,et al.  Multiple network fusion using fuzzy logic , 1995, IEEE Trans. Neural Networks.

[12]  Masami Ito,et al.  Task decomposition and module combination based on class relations: a modular neural network for pattern classification , 1999, IEEE Trans. Neural Networks.

[13]  Fakhri Karray,et al.  Feature-based decision aggregation in modular neural network classifiers , 1999, Pattern Recognit. Lett..

[14]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[15]  B. Yegnanarayana,et al.  Recognition of Stop-Consonant-Vowel (SCV) segments in continuous speech using neural network models , 1996 .

[16]  Kishan G. Mehrotra,et al.  An improved algorithm for neural network classification of imbalanced training sets , 1993, IEEE Trans. Neural Networks.

[17]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[18]  B. Yegnanarayana,et al.  Modular networks and constraint satisfaction model for recognition of stop consonant-vowel (SCV) utterances , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[19]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

[20]  Michael I. Jordan,et al.  Learning piecewise control strategies in a modular neural network architecture , 1993, IEEE Trans. Syst. Man Cybern..