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دسته بندی:
داده کاوی - data mining
سال انتشار:
2019
عنوان انگلیسی مقاله:
Implementation of nature-inspired optimization algorithms in some data mining tasks
ترجمه فارسی عنوان مقاله:
اجرای الگوریتم های بهینه سازی با الهام از طبیعت در برخی از کارهای داده کاوی
منبع:
Sciencedirect - Elsevier - Ain Shams Engineering Journal, Corrected proof. doi:10.1016/j.asej.2019.10.003
نویسنده:
A.M. Hemeida a,⇑, Salem Alkhalaf b, A. Mady c, E.A. Mahmoud c, M.E. Hussein c, Ayman M. Baha Eldin d
چکیده انگلیسی:
Data mining optimization received much attention in the last decades due to introducing new optimization
techniques, which were applied successfully to solve such stochastic mining problems. This paper
addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data
sets in machine learning by implementing four different optimization techniques. The selected data sets
used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In
the classification problem of this paper, the neural network (NN) is used with four optimization techniques,
which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization
(MVA), and grey wolf optimization (GWO). Different control parameters were considered for
accurate judgments of the suggested optimization techniques. The comparitive study proves that, the
GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification
rate, and MSE.
Keywords: Data mining | Optimization | Evolutionary computation | Multi-layer perceptron | Metaheuristics
قیمت: رایگان
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