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S.B. Gelfand, C.S. Ravishankar, E.J. Delp, "An Iterative Growing and Pruning Algorithm for Classification Tree Design," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 163174, February, 1991.  
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@article{ 10.1109/34.67645, author = {S.B. Gelfand and C.S. Ravishankar and E.J. Delp}, title = {An Iterative Growing and Pruning Algorithm for Classification Tree Design}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {13}, number = {2}, issn = {01628828}, year = {1991}, pages = {163174}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.67645}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  An Iterative Growing and Pruning Algorithm for Classification Tree Design IS  2 SN  01628828 SP163 EP174 EPD  163174 A1  S.B. Gelfand, A1  C.S. Ravishankar, A1  E.J. Delp, PY  1991 KW  pattern recognition; estimation theory; Bayes methods; pruning algorithm; classification tree design; rightsized trees; stopping rules; terminal nodes; iterative method; waveform recognition problem; Bayes methods; estimation theory; iterative methods; pattern recognition; trees (mathematics) VL  13 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A critical issue in classification tree designobtaining rightsized trees, i.e. trees which neither underfit nor overfit the datais addressed. Instead of stopping rules to halt partitioning, the approach of growing a large tree with pure terminal nodes and selectively pruning it back is used. A new efficient iterative method is proposed to grow and prune classification trees. This method divides the data sample into two subsets and iteratively grows a tree with one subset and prunes it with the other subset, successively interchanging the roles of the two subsets. The convergence and other properties of the algorithm are established. Theoretical and practical considerations suggest that the iterative free growing and pruning algorithm should perform better and require less computation than other widely used tree growing and pruning algorithms. Numerical results on a waveform recognition problem are presented to support this view.
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