Pruning_Decision_Trees

Pruning Decision Trees Review Article Pruning Decision Trees Introduction Expert Systems often utilize massive data structures to store information that later need to be searched. These data structures, called decision trees, are in most cases incomprehensible to a user due to their complexity. Methods of prioritizing, storing, and manipulating these structures have been the subject of exhaustive research. In effort to reduce search time, many algorithms have been invented which simplify, or prune, these trees. There are a large number of ways to implement these algorithms. A good pruning algorithm can have the dual effect of both decreasing the size of a tree and increasing the accuracy or of a search on the tree. This review article will provide interested parties with a concise overview of current pruning methods, a discussion of the tradeoffs between simplicity and accuracy, as well as introducing some new findings in this field of research. Background Most pruning algorithms have many things in common. The basic pruning approach involves replacing a subtree with a leaf node determined by the most common class that was a subset of the original subtree. The pruned tree is tested and verified by a representative validation set. A pruning, tree, error, trees, methods, accuracy, set, pruned, nodes, data, method, algorithms, size, node, based, algorithm, validation, new, decision, been, subtree, leaf, however, two, subtrees, review, pep, often, mccp, knowledge, cost, complexity, common, bottom, using