A study on evolutionary design of binary decision trees (1999) [2 citations — 2 self]
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Abstract:
Abstract- For pattern recognition, the decision trees (DTs) are more efficient than neural networks (NNs) for two reasons. First, the computations in making decisions are simpler. Second, important features can be selected automatically during the design process. However, the DTs are not adaptable. This problem can be avoided by mapping a DT to an NN. This mapping not only makes a DT adaptable, but also provides a systematic way for determining the NN structure. In addition, since the features are well selected, the NN obtained from this mapping may have much fewer connections than those designed directly. The key point here is to design a DT which is as small as possible. In this paper, we study the evolutionary design of the decision trees, and investigate some methods to improve the design efficiency.
Citations
| 2489 | Induction of Decision Trees – Quinlan - 1986 |
| 135 | Constructing optimal binary decision trees is npcomplete – Hyafil, Rivest - 1976 |
| 54 | An iterative growing and pruning algorithm for classification tree design – Gelfand, Ravishankar, et al. - 1984 |
| 51 | Entropy nets: From decision trees to neural networks – Sethi - 1990 |
| 34 | Genetic programming using a minimum description length principle – Iba, Garis, et al. - 1994 |
| 17 | A nonparametric partitioning procedure for pattern classification – HENRICHON, FU - 1969 |
| 12 | A partitioning algorithm with application in pattern classification and the optimization of decision trees – Meisel, Michalopoulos - 1973 |
| 1 | et al, "Automatic design of binary decision trees based on genetic programming – Shirasaka, Zhao - 1998 |
