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Handling mixed categorical and numerical features in machine learning techniques / Dina Tantawy Hassan Saleh ; Supervised Amir Fouad Sorial Atiya , Olfat Gamil Shaker

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Dina Tantawy Hassan Saleh , 2016Description: 94 P. : charts , facsimiles ; 30cmOther title:
  • معالجة الخصائص الرقمية والفئوية المختلطة في طرق تعليم الآلة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: Most 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
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Di.H (Browse shelf(Opens below)) Not for loan 01010110069998000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Di.H (Browse shelf(Opens below)) 69998.CD Not for loan 01020110069998000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Most 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

Issued also as CD

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