Associative classification using patterns from nested granules. (22nd November 2010)

• Record Type: Journal Article
• Authors: Lui, Thomas W.H.; Chiu, David K.Y.
• Is Part Of: International journal of granular computing, rough sets and intelligent systems. Volume 1:Number 4(2010)
• Page Start: 393
• Page End: 406
• Publication Date: 2010-11-22
• Subjects: information granules -- high-order patterns -- HOP -- nested high-order patterns -- NHOP -- associative classification -- granular computing -- nested granules; Intelligent agents (Computer science) -- Periodicals; 006.3
• Languages: English
• ISSNs: 1757-2703

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.