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In this paper we propose an improved inductive learning method, IP2, to derive classification rules that correctly describe most of the examples belonging to a class and do not describe most of the examples not belonging to this class. A pre-analysis of data is included that assigns higher weights to those values of attributes which occur more often in the positive than in the negative examples. The...
We propose an improved inductive learning procedure, IPI, to derive classification rules from examples. A preanalysis of data is included which assigns higher weights to those values of the attributes which occur more often in the positive than in the negative examples. The inductive learning problem is represented as a covering problem in integer programming which is solved by a modified greedy algorithm...
We propose an improved inductive learning method to derive classification rules correctly describing (at least) most of the positive examples and do not correctly describe (at least) most of the negative examples. We start with a pre-analysis of data to assign higher weights to those values of attributes which occur more often in the positive than in the negative examples. The inductive learning problem...
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