The revision of inductive learning theory within incomplete and imprecise observations1
The system described here is concerned with the revision of inductive learning theory, i.e. the induced concept representation is updated and extended by taking into account new observations, as yet unlearned by the system. One of the aims of this paper is to show that it is feasible to classify observations, which have a great number of unknown values (incomplete information), as well as to estimate those values by exploiting pertinently known values. Imprecise information is also envisaged. The system works in two stages. Stage One applies a non-incremental unsupervised induction method, which is a learning cycle, make up of three steps: (i) verification of input information; (ii) classification of observations; (iii) organization of the information into hierarchies of clusters, according to concept formation. Stage Two is an incremental unsupervised induction method, which classifies new observations allowing for the later inclusion of currently unknown values. In both stages, the system explores the elements of each cluster in order to discover their relationship. Another interesting point in our proposal is that its involved algorithms have polynomial complexity in order to deal with large databases. The whole mechanism is illustrated throughout this paper by making reference to experimental results obtained in the Periodic Table of Elements, PTE.
induction learning theory concept formation algorithms conceptual clustering