An Integer Programming Approach to Inductive Learning Using Genetic and Greedy Algorithms
Janusz Kacprzyk, Grażyna Szkatuła · Studies in fuzziness and soft computing · 2002
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 inductive learning problem is represented as a modification of the set covering problem which are solved by an integer programming based algorithm using elements of a greedy algorithm or a genetic algorithm. The results are very encouraging and are illustrated on thyroid cancer and coronary heard disease problems.