Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory

Hierarchical neural networks show many benefits when employed for classification problems even when only simple methods analogous to decision trees are used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neural networks the usage of Dempster-Shafer evidence theory suggests itself as it allows for the representation of evidence at different levels of abstraction. Moreover, it provides the possibility to differentiate between uncertainty and ignorance. The proposed approach has been evaluated using three different data sets and showed consistently improved classification results compared to the simple decision-tree-like retrieval method.

[1]  Alessandro Saffiotti,et al.  The Transferable Belief Model , 1991, ECSQARU.

[2]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[3]  Friedhelm Schwenker,et al.  Three learning phases for radial-basis-function networks , 2001, Neural Networks.

[4]  Günther Palm,et al.  Hierarchical Object Classification for Autonomous Mobile Robots , 2002, ICANN.

[5]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[6]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[7]  Joydeep Ghosh,et al.  Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis , 2002, Pattern Analysis & Applications.

[8]  Joydeep Ghosh,et al.  Integrating support vector machines in a hierarchical output space decomposition framework , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

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

[10]  Soo-Young Lee,et al.  Support Vector Machines with Binary Tree Architecture for Multi-Class Classification , 2004 .

[11]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[12]  E. Mandler,et al.  Combining the Classification Results of Independent Classifiers Based on the Dempster/Shafer Theory of Evidence , 1988 .

[13]  Friedhelm Schwenker,et al.  Solving Multi-class Pattern Recognition Problems with Tree-Structured Support Vector Machines , 2001, DAGM-Symposium.

[14]  Joydeep Ghosh,et al.  An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems , 2004, Multiple Classifier Systems.

[15]  Günther Palm,et al.  Using Dempster-Shafer Theory in MCF Systems to Reject Samples , 2005, Multiple Classifier Systems.

[16]  R. Fildes Journal of the Royal Statistical Society (B): Gary K. Grunwald, Adrian E. Raftery and Peter Guttorp, 1993, “Time series of continuous proportions”, 55, 103–116.☆ , 1993 .

[17]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[18]  U. Kressel The Impact of the Learning–Set Size in Handwritten–Digit Recognition , 1991 .

[19]  Mohamed A. Deriche,et al.  A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence , 2002, J. Artif. Intell. Res..

[20]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Eibe Frank,et al.  Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms , 2004, PAKDD.

[22]  Isabelle Bloch,et al.  Sensor fusion in anti-personnel mine detection using a two-level belief function model , 2003, IEEE Trans. Syst. Man Cybern. Part C.