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ThinkMind // IMMM 2014, The Fourth International Conference on Advances in Information Mining and Management // View article immm_2014_1_20_50063


Class Stength Prediction Method for Associative Classification

Authors:
Suzan Ayyat
Joan Lu
Fadi Thabtah

Keywords: associative classification; data mining; prediction phase

Abstract:
Abstract—Test data prediction is about assigning the most suitable class for each test case during classification. In Associative Classification (AC) data mining, this step is considered crucial since the overall performance of the classifier is heavily dependent on the class assigned to each test case. This paper investigates the classification (prediction) step in AC in an attempt to come up with a novel generic prediction method that assures the best class assignment for each test case. The outcome is a new prediction method that takes into account all applicable rules ranking position in the classifier beside the class number of rules. Experimental results using different data sets from the University of California Irvine (UCI) repository and two common AC prediction methods reveal that the proposed method is more accurate for the majority of the data sets. Further, the proposed method can be plugged and used successfully by any AC algorithm.

Pages: 5 to 10

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-364-3

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014

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