Intellectual property core implementation of decision trees

Several soft intellectual property (IP) core implementations of decision trees (axis-parallel, oblique and nonlinear) based on the concept of universal node (UN) and sequence of UNs are presented. Proposed IP cores are suitable for implementation in both field programmable gate arrays and application specific integrated circuits. Developed IP cores can be easily customised in order to fit a wide variety of application requirements, fulfilling their role as general purpose building blocks for SoC designs. Experimental results obtained on 23 data sets of standard UCI machine learning repository database suggest that the proposed architecture based on the sequence of UNs requires on average 56% less hardware resources compared with previously proposed architectures, having the same throughput.

[1]  S. Himavathi,et al.  Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization , 2007, IEEE Transactions on Neural Networks.

[2]  René Cumplido,et al.  Decision Tree Based FPGA-Architecture for Texture Sea State Classification , 2006, 2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006).

[3]  Amine Bermak,et al.  A compact 3D VLSI classifier using bagging threshold network ensembles , 2003, IEEE Trans. Neural Networks.

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  Steven L. Salzberg,et al.  On growing better decision trees from data , 1996 .

[7]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[8]  Z. M. Hefed Object tracking , 1999 .

[9]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Davide Anguita,et al.  Feed-Forward Support Vector Machine Without Multipliers , 2006, IEEE Transactions on Neural Networks.

[12]  D. C. Hendry,et al.  IP core implementation of a self-organizing neural network , 2003, IEEE Trans. Neural Networks.