Changes between Version 33 and Version 34 of Parallel-DT

Timestamp:

Jan 18, 2010, 9:21:18 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)]]