An inductive learning algorithm with a partial completeness and consistence via a modified set covering problem

Authors:
Janusz Kacprzyk;Grazyna Szkatuła
Affiliations:
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 3
Cited 1

An integer programming approach to inductive learning using genetic and greedy algorithms

New learning paradigms in soft computing
An Improved Inductive Learning Algorithm with a Preanalysis of Data

ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
A softened formulation of inductive learning and its use for coronary disease data

ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems

An approach to dimensionality reduction in time series

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an inductive learning algorithm that allows for a partial completeness and consistence, i.e. that derives classification rules correctly describing, e.g, most of the examples belonging to a class and not describing most of the examples not belonging to this class. The problem is represented as a modification of the set covering problem that is solved by a greedy algorithm. The approach is illustrated on some medical data.