Graph-based representation has been successfully used to support various machine learning and data mining algorithms. The learning algorithms strongly rely on the algorithm employed for constructing the graph from input data, given as a set of vector-based patterns. A popular way to build such graphs is to treat each data pattern as a vertex; vertices are then connected according to some similarity measure, resulting in an structure known as data graph. In this paper we propose a new type of data graph, focused on data attributes, named Attribute-based Decision Graph — AbDG, suitable for supervised multiclass classification tasks. The input data for constructing an AbDG is a set of data-vectors (patterns), that can be described by either type of attributes (numeric, categorical or both). Also, algorithms for constructing such graphs and using them in classification tasks are described. An AbDG can be associated to a classifying procedure approached as a graph matching process, where the sub-graph representing a new pattern is matched against the AbDG. The proposed approach has been experimentally evaluated on classification tasks in twenty knowledge domains and the results are competitive when compared to those of two well-known classification methods (C4.5 and Multi-Interval ID3).
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.