Hierarchical classification with a competitive evolutionary neural tree

A new, dynamic, tree structured network, the Competitive Evolutionary Neural Tree (CENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that the CENT offers over other hierarchical competitive networks is its ability to self determine the number, and structure, of the competitive nodes in the network, without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated heuristics. The results of network simulations are presented over a range of data sets, including Anderson's IRIS data set. The CENT network demonstrates its ability to produce a representative hierarchical structure to classify a broad range of data sets.

[1]  Neil Davey,et al.  A Comparative Study of two Self Organising and Structurally Adaptive Dynamic Neural Tree Networks , 1995 .

[2]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[3]  Neil Davey,et al.  The development of a software clone detector , 1995 .

[4]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[5]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[6]  Peter H. A. Sneath,et al.  Numerical Taxonomy: The Principles and Practice of Numerical Classification , 1973 .

[7]  Franklin A. Graybill,et al.  Introduction to The theory , 1974 .

[8]  E. Clothiaux,et al.  Neural Networks and Their Applications , 1994 .

[9]  Janos Racz,et al.  Knowledge representation by dynamic competitive learning techniques , 1991, Defense + Commercial Sensing.

[10]  Y. Y. Tang,et al.  A structurally adaptive neural tree for the recognition of large character set , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[11]  P. Sopp.  Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[12]  Tao Li,et al.  Hierarchical classification and vector quantization with neural trees , 1993, Neurocomputing.

[13]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[14]  Brian Everitt,et al.  Cluster analysis , 1974 .

[15]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[16]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[17]  Allen M. Peterson,et al.  Adaptive Vector Quantization Using a Self-Development Neural Network , 1990, IEEE J. Sel. Areas Commun..