A Novel Multiway Splits Decision Tree for Multiple Types of Data

Algorithm 2

MSDT.

	Input: training set and the threshold value minparent
	Output: the decision tree
(1)	Create node according to the instances in .
(2)	Procedure grow(node)
(3)	If is less than the minparent or the instances are not partitionable (All the instances are of the same class or have the same feature values) Then
(4)	mark node as a leaf, and label it with the class of the majority of instances in .
(5)	Return node
(6)	End If
(7)	Call multi_split to get the cluster , , and .
(8)	Save the values of , and into the current node for the prediction.
(9)	For i = 1 to Do
(10)	Create node_i according to instances in , call grow(node_i).
(11)	End For
(12)	End Procedure
(13)	Prune the node-rooted tree by pessimistic pruning algorithm.
(14)	Return the node-rooted decision tree.