کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387716 660906 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification based on specific rules and inexact coverage
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Classification based on specific rules and inexact coverage
چکیده انگلیسی

Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature.


► Advantages of specific rules with high confidence in classification based on CARs.
► Avoid ambiguity at the classification stage may increase the classification accuracy.
► A new coverage strategy, during the classification stage is proposed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 12, 15 September 2012, Pages 11203–11211
نویسندگان
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