Title: Associative classification using patterns from nested granules

Authors: Thomas W.H. Lui, David K.Y. Chiu

Addresses: 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.

DOI: 10.1504/IJGCRSIS.2010.036981

International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1 No.4, pp.393 - 406

Available online: 20 Nov 2010 *

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