TY  - BOOK
AU  - Dina Tantawy Hassan Saleh
AU  - Amir Fouad Sorial Atiya , 
AU  - Olfat Gamil Shaker , 
TI  - Handling mixed categorical and numerical features in machine learning techniques / 
PY  - 2016///
CY  - Cairo : 
PB  - Dina Tantawy Hassan Saleh , 
KW  - Classi{uFB01}cation
KW  - Machine Learning
KW  - Mixed Categorical and Numerical
N1  - Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering; Issued also as CD
N2  - 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
UR  - http://172.23.153.220/th.pdf
ER  -