000			03038cam a2200337 a 4500
003			EG-GiCUC
005			20250223032720.0
008			210327s2020 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.03.M.Sc.2020.Ma.R
100	0		_aMariam Moustafa Reda Abdallah Eltantawi
245	1	2	_aA recommender tool for classification algorithms / _cMariam Moustafa Reda Abdallah Eltantawi ; Supervised Akram Salah , Mohammed Nassef
246	1	5	_aأداة تزكية للخوارزميات التصنيفية
260			_aCairo : _bMariam Moustafa Reda Abdallah Eltantawi , _c2020
300			_a104 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science
520			_aIn the process of selecting the most appropriate classification algorithm, there are two main tasks. The determination of the factors that will be used in the selection process and the methodology that will be used to make use of these factors and decide upon the most appropriate algorithm to solve the problem. The first task is to characterize datasets; by extracting its characteristics/meta-data or by adopting a reasonable characterization approach, whilst the second task is the learning and deciding task based on the characterization. Choosing the most appropriate classification algorithm for classification problem is becoming a strategically important task in the data-mining process. Due to the availability of numerous classification algorithms in the area of data mining for solving the same kind of problem, with little guidance available for recommending the most appropriate algorithm to use which gives best results for the dataset at hand, this task becomes more and more complicated. As a way of optimizing the chances of recommending the most appropriate classification algorithm for a dataset, a two-step study was conducted: (1) Survey study focusing on the different factors considered by data miners and researchers in different studies when manually selecting the classification algorithms that will yield desired knowledge for the dataset at hand, a categorization tree was created for the measurable factors.The categorization tree, groups and categorizes these factors so that they can be exploited by recommendation software tools. (2) Experimental study of an automated tool based on a pure Collaborative Filtering Recommender System, User-Item approach, to recommend the most appropriate classification algorithm for a classification problem, relying on historical datasets meta-data
530			_aIssued also as CD
653		4	_aAlgorithm selection
653		4	_aClassification
653		4	_aData mining
700	0		_aAkram Salah , _eSupervisor
700	0		_aMohammed Nassef , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c80392 _d80392