  An approach to the classification problem of machine learning, based on
building local classification rules, is developed. The local rules are
considered as projections of the global classification rules to the event we
want to classify. A massive global optimization algorithm is used for
optimization of quality criterion. The algorithm, which has polynomial
complexity in typical case, is used to find all high--quality local rules. The
other distinctive feature of the algorithm is the integration of attributes
levels selection (for ordered attributes) with rules searching and original
conflicting rules resolution strategy. The algorithm is practical; it was
tested on a number of data sets from UCI repository, and a comparison with the
other predicting techniques is presented.
