Mostexisting decision tree systemsuse a greedyapproachto inducetrees -- locally optimalsplits are inducedat every node of the tree. Althoughthe greedy approachis suboptimal,it is ...
A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attr...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
We study the possibility of constructing decision trees with evolutionary algorithms in order to increase their predictive accuracy. We present a self-adapting evolutionary algori...