Binary Decision Tree Synthesis: Splitting Criteria and the Algorithm LISTBB

In our days, interest to the class of inductors on the basis of decision trees does not weaken, especially in the context of Data Mining paradigm . At the same time most widespread Quinlan algorithms ID3 and C4.5, as we show in the paper, are not the best. It is therefore possible to see the successful attempts of creation another heuristic splitting criteria for the algorithms of synthesis of decision trees. Comparative definition of different splitting criteria used for the synthesis of binary decision trees is the purpose of the paper. We included the criteria $D,\Omega ,Z_{1}$ and other which were developed by the author yet at 1979-80 years. These criteria define combined splitting principle which is used in the algorithm LISTBB.