کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382297 660755 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving the performance of inductive learning classifiers through the presentation order of the training patterns
ترجمه فارسی عنوان
بهبود عملکرد طبقه بندی یادگیری استقرایی از طریق سفارش ارائه شده الگوهای آموزش
کلمات کلیدی
یادگیری استقرایی؛ خانواده قوانین؛ خوشه بندی؛ تقسیم بندی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Presentation order of the training patterns affects inductive learning classifiers.
• An intelligent presentation order method is proposed.
• Random presentation order generates a variation in the classification of about 7%.
• The proposed method obtained on average better classifications than random orders.

Although the development of new supervised learning algorithms for machine learning techniques are mostly oriented to improve the predictive power or classification accuracy, the capacity to understand how the classification process is carried out is of great interest for many applications in business and industry. Inductive learning algorithms, like the Rules family, induce semantically interpretable classification rules in the form of if-then rules. Although the effectiveness of the Rules family has been studied thoroughly and new and improved versions are constantly been developed, one important drawback is the effect of the presentation order of the training patterns which has not been studied in depth previously. In this paper this issue is addressed, first by studying empirically the effect of random presentation orders in the number of rules and the generalization power of the resulting classifier. Then a presentation order method for the training examples is proposed which combines a clustering stage with a new density measure developed specifically for this problem. The results using benchmark datasets and a real application of wood defect classification show the effectiveness of the proposed method. Also, since the presentation order method is employed as a preprocessing stage, the simplicity of the Rules family is not affected but instead it enables the generation of fewer and more accurate rules, which can have a direct impact in the performance and usefulness of the Rules family in an expert system context.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 58, 1 October 2016, Pages 1–9
نویسندگان
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