Effect of the x2 test on construction of ID3 decision trees
Mayank Thakore, Daniel C. St. Clair · 1993
Quinlan's 1D3 machine learning algorithm induces classification trees (rules) from a set of Eaining examples.The algorithm is extremely effective when training examples contain little or no noise.Noisy training data may result in the induction of decision trees which are not representative of the domain being modeled, To reduce the effect of noise on ID3's construction of decision trees, Quinlarr suggested the use of the Z2 statistic as a tool for identifying noise in training attributes.Attributes appearing ttr be noisy can then be excluded from consideration as branching attributes.Since an a[fribute may appear more than cmce in a tree, an attribute musl be tested for noise each time it is under consideration.The work presenled in this paper evaluates the effect of using the Z* statistic as a tool for identifying noise during ID3 tree construction.[t was found that the effects of this approach vary Jepending on the classifl( wiur.criterion used to match tree and test example conclusions.Numerical results are provided which illustrate these differences.