Decision tree incremental learning method oriented to information big data
A technology of incremental learning and decision tree, applied in machine learning, computing model, computing and other directions, can solve problems such as unacceptable, reduced classification accuracy, and huge decision tree.
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[0024] Combine below figure 1 , give an example to describe the present invention in more detail.
[0025] Step 1, node n 0 as the root node of the decision tree T. calculate n 0 The node splitting metric SC(n 0 ), if n 0 is a separable node, then the n 0 Put it into the set Q of nodes to be split. The node splitting criterion is in refers to the node that belongs to n i The number of records, MG(n i ) is the node n i Maximum information gain when splitting into two branches.
[0026] Step 2. If the number of leaf nodes in the decision tree T is less than the limited maximum number of leaf nodes and the set Q is not empty, repeat the following operations for all nodes in the set Q;
[0027] Step 3: From the candidate classification node set Q, select the node n with the largest splitting metric value b , and the node n b Deleted from set Q.
[0028] Step 4. Split node n b , and compute the split n b The node splitting metrics of the two child nodes generated...
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