Changes between Version 35 and Version 36 of Parallel-DT

Timestamp:

Jan 18, 2010, 9:23:14 PM (14 years ago)

Author:

andrei.minca

Comment:

--

Parallel-DT

@@ -28 +28 @@
 
 In this approach, all processors construct a decision tree syncronously by sending and receiving class distribution information of local data. Major steps for the approach:
-   ** select a node to expand according to a decision tree expansion strategy (eg Depth-First or Breadth-First), and call that node as the current node. At the beginning, root node is selected as the current node
-   ** for each data attribute, collect class distribution information of the local data at the current node
-   ** exchange the local class distribuition information using global reduction among processors
-   ** simultaneously compute the entropy gains of each attribute at each processor and select the best attribute for child node expansion
-   ** depending on the branching factor of the tree desired, create child nodes for the same number of partitions of attributes values, and split training cases accordingly
+   * * select a node to expand according to a decision tree expansion strategy (eg Depth-First or Breadth-First), and call that node as the current node. At the beginning, root node is selected as the current node
+   * * for each data attribute, collect class distribution information of the local data at the current node
+   * * exchange the local class distribuition information using global reduction among processors
+   * * simultaneously compute the entropy gains of each attribute at each processor and select the best attribute for child node expansion
+   * * depending on the branching factor of the tree desired, create child nodes for the same number of partitions of attributes values, and split training cases accordingly
 
 [[Image(SyncronusTreeConstruction-DepthFirstExpansionStrategy.jpg)]]