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Comparative Analysis of Machine Learning Algorithms with Optimization Purposes | ||
| Control and Optimization in Applied Mathematics | ||
| مقاله 5، دوره 1، شماره 2، دی 2016، صفحه 63-75 اصل مقاله (553.18 K) | ||
| نوع مقاله: Research Article | ||
| نویسنده | ||
| Rohollah Alesheykh* | ||
| Payame Noor University | ||
| چکیده | ||
| The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data. In this paper, a methodology has been employed to optimize the precision of defect detection of concrete slabs depending on their qualitative evaluation. Based on this idea, some machine learning algorithms such as C4.5 decision tree, RIPPER rule learning method and Bayesian network have been studied to explore the defect of concrete and to supply a decision system to speed up the defect detection process. The results from the examinations show that the proposed RIPPER rule learning algorithm in combination with Fourier Transform feature extraction method could get a defect detection rate of 93% as compared to other machine learning algorithms. | ||
| کلیدواژهها | ||
| decision tree؛ Bayesian network؛ rule learning algorithm؛ Optimization؛ Soft Computing | ||
| عنوان مقاله [English] | ||
| تحلیل تطبیقی الگوریتمهای یادگیری ماشین با اهداف بهینهسازی | ||
| نویسندگان [English] | ||
| روح الله آل شیخ | ||
| دانشگاه پیام نور | ||
| چکیده [English] | ||
| مبحث بهینهسازی و یادگیری ماشین بهصورت گستردهای بههم مرتبط هستند و بهینهسازی در مسایل مختلف منجر به استفاده از روشهای یادگیری ماشین میگردد. الگوریتمهای یادگیری ماشین برای کلاسهای ویژهای از مسایل در یک زمان محاسباتی منطقی کار میکنند و نقش مهمی در استخراج دانش از حجم انبوهی از دادهها دارند. در این مقاله یک روش برای بهینهسازی دقت تشخیص نقص قطعههای بتنی بر اساس ازریابی کیفی آنها بهکار گرفته شده است. بر این اساس، چند الگوریتم یادگیری ماشین از جمله درخت تصمیمگیری C4.5 ، روش یادگیری قاعده ریپر و شبکه بیزین، برای بررسی نقص در بتن مورد مطالعه قرار گرفتهاند تا یک سیستم تصمیمگیری برای سرعت بخشیدن به فرآیند تشخیص نقص مهیا گردد. نتایج آزمایشها نشان میدهد که میزان تشخیص نقص 93 درصد با استفاده از الگوریتم یادگیری قاعده ارائه شده به همراه روش استخراج ویژگی تبدیل فوریه در مقایسه با سایر الگوریتمهای یادگیری ماشین حاصل شده است. | ||
| کلیدواژهها [English] | ||
| درخت تصمیمگیری, شبکه بیزین, روش یادگیری قاعده, بهینهسازی, محاسبات نرم | ||
| مراجع | ||
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