Supervised Learning Using Instance-based Patterns

Abstract

This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done using standard distance metrics, a strength function and an exclusive values concept. Classification tests done on several data files have shown very accurate results.