Title: Associative classification using patterns from nested granules
Author: Thomas W.H. Lui, David K.Y. Chiu
Department of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada.
Department of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada
Abstract: To facilitate interpretation and consider the internal association relationships between values of a pattern used in associative classification, a new form of multiple value pattern known as nested high-order pattern (NHOP) is presented. Taking an associative pair as information granule, the pattern is formed as multiple levels of association events. The general form of high-order pattern (HOP), that NHOP is a subtype, is identified as variable outcomes extracted from a random N-tuple. The pattern is detected by statistical testing if the occurrence is significantly deviated from the expected according to a prior model or null hypothesis. In this paper, we propose a classification method (called C-NHOP) based on nested high-order patterns. The rationale is that complex association patterns reinforce the underlying meaningfulness in interpreting regularity, thus, can provide a better understanding of the data domain. In evaluating our method using 26 UCI machine learning benchmark datasets, the experiments show a highly competitive and interpretable result.
Keywords: information granules; high-order patterns; HOP; nested high-order patterns; NHOP; associative classification; granular computing; nested granules.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1, No.4, pp.393 - 406
Available online: 20 Nov 2010