During the decision tree learning process, the algorithm detects the better variable according to a goodness of fit measure when it tries to split a node. The calculation can take a long time, particularly when it deals with a continuous descriptors for which it must detect the optimal cut point.
For all the decision tree algorithms, Sipina can use a local sampling option when it searches the best splitting attribute on a node. The idea is the following: on a node, it draws a random sample of size n, and then all the computations are made on this sample. Of course, if n is lower than the number of the existing examples on the node, Sipina uses all the available examples. It occurs when we have a very large tree with a high number of nodes.
We have described this approach in a paper (Chauchat and Rakotomalala, IFCS-2000) . We describe in this tutorial how to implement it with Sipina. We note in this tutorial that using a sample on each node enables to reduce dramatically the execution time without loss of accuracy.
We use a version of the WAVEFORM dataset with 21 continuous descriptors and 2,000,000 instances. We obtain the tree in 3 seconds on our computer.
Keywords : decision tree, sampling, large dataset
Components : SAMPLING, ID3, TEST
Tutorial : en_Sipina_Sampling.pdf
Dataset : wave2M.zip
Références :
J.H. Chauchat, R. Rakotomalala, « A new sampling strategy for building decision trees from large databases », Proc. of IFCS-2000, pp. 199-204, 2000.
