000			02041cam a2200337 a 4500
003			EG-GiCUC
005			20250223031618.0
008			161124s2016 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.06.M.Sc.2016.Di.H
100	0		_aDina Tantawy Hassan Saleh
245	1	0	_aHandling mixed categorical and numerical features in machine learning techniques / _cDina Tantawy Hassan Saleh ; Supervised Amir Fouad Sorial Atiya , Olfat Gamil Shaker
246	1	5	_aمعالجة الخصائص الرقمية والفئوية المختلطة في طرق تعليم الآلة
260			_aCairo : _bDina Tantawy Hassan Saleh , _c2016
300			_a94 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering
520			_aMost classi{uFB01}ers cannot handle mixed categorical and numerical features directly. Each classi{uFB01}er is usually built for only one type of features either categorical or numerical ones. This study analyzes the handling technique using mid-sized data on several di{uFB00}erent classi{uFB01}ers. It also introduce a number of new techniques like dynamic simplex, catRank, Joint measure and devise the proof for kernelized versions of IOF, OF, Goodall, Lin and overlap measures. A case study on breast cancer prediction is also provided with some recommendation for both medical and machine learning parts. The study reveals an association between classi{uFB01}ers and techniques and provides some recommendation for handling techniques usage
530			_aIssued also as CD
653		4	_aClassi{uFB01}cation
653		4	_aMachine Learning
653		4	_aMixed Categorical and Numerical
700	0		_aAmir Fouad Sorial Atiya , _eSupervisor
700	0		_aOlfat Gamil Shaker , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamah _eCataloger
942			_2ddc _cTH
999			_c58763 _d58763