Using attribute dependencies for rule learning
Knowledge representation and organization in machine learning
An overview of the LERS1 learning system
IEA/AIE '89 Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Principles of Database Systems
Principles of Database Systems
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The paper discusses two programs for learning from examples, LEM (Learning from Examples Module) and LERS (Learning from Examples based on Rough Sets). A few versions of both programs are implemented in Franz Lisp and are running on VAX 11/780. Both programs' main task is to automate knowledge acquisition for expert systems. Hence, they produce rules in the minimal discriminant form. The main problem addressed in the paper is the selection of the best mechanism for determining coverings, the minimal sets of relevant attributes. Four different methods, based on indiscernibility relation, partition, characteristic set and lower boundary are compared. Both theoretical analysis and experimental results of multiple running of many sets of examples, with variable number of examples and with variable number of attributes are taken into account. As a result the partition method is determined to be the most efficient way to compute coverings.