\documentclass[12pt]{report} \topmargin=0in \headheight=0in \headsep=0in \oddsidemargin=0in \evensidemargin=0in \textwidth=6.5in \textheight=9in \usepackage{setspace} \usepackage{graphicx} \usepackage{algorithmic,cite,footmisc,multirow,url} \usepackage{color} \usepackage{pslatex} \usepackage{fullpage} \usepackage{listings} \usepackage{float} \usepackage{epsfig} \usepackage[table]{xcolor} \usepackage{amsmath, amsthm, amssymb} \definecolor{light-gray}{gray}{0.95} \newcommand{\bb}[1]{\cellcolor[gray]{#1}{\color{white}{\textbf{\large #1}}}} \newcommand{\br}[1]{\cellcolor[gray]{#1}} \newcommand{\cl}{\color{white}} \newcommand{\cb}[1]{\color{black}{\textbf{\large #1}}} \newcommand{\hr}{\hline \hline} \newcommand{\rg}{\multicolumn{1}{|c|}{Range}} \newcommand{\rk}{\multicolumn{1}{|c|}{Rank}} \newcommand{\model}[1]{\multicolumn{10}{|c|}{\cb{#1}}} \newcommand{\resultbox}{\scalebox{0.55}} \newcommand{\nbc}{~Na\"{i}ve Bayes~} \newcommand{\bi}{\begin{itemize}} \newcommand{\ei}{\end{itemize}} \newcommand{\be}{\begin{enumerate}} \newcommand{\ee}{\end{enumerate}} \newcommand{\bd}{\begin{description}} \newcommand{\ed}{\end{description}} \newcommand{\hyp}[1]{Hypothesis~\ref{hyp:#1}} \newcommand{\app}[1]{Appendix~\ref{a:#1}} \newcommand{\fig}[1]{Figure~\ref{fig:#1}} \newcommand{\tab}[1]{Table~\ref{tb:#1}} \newcommand{\eq}[1]{Equation~\ref{eq:#1}} \newcommand{\sect}[1]{Section~\ref{#1}} \newcommand{\chap}[1]{Chapter~\ref{#1}} \newcommand{\dbsp}{\linespread{1.5} \rm} \newcommand{\ssp}{\linespread{1.0} \rm} \newcommand{\att}{~AT\&T~} \newcounter{over} \newcounter{max} \newcommand{\fred}[2]{\setcounter{over}{#2}\addtocounter{over}{#1}} \newcommand{\boxplot}[5]{% \scalebox{0.5}{\textcolor{white}{\setcounter{max}{#4}\addtocounter{max}{#5}\fred{#4}{-#2}}% \begin{picture}(100,10)% \put(0,0){\line(0,1){8}}% \put(100,0){\line(0,1){8}}% %\put(#1,4){\circle*{1}}% %\put(\themax,4){\circle*{1}}% \put(#2,4){\line(1,0){\theover}}% \put(#3,4){\circle*{8}}% %\put(#4,4){\line(1,0){#5}}% \put(50,0){\line(0,1){8}}% \end{picture}} } \usepackage{eso-pic} \usepackage{color} \usepackage{type1cm} %\makeatletter % \AddToShipoutPicture{% % \setlength{\@tempdimb}{.5\paperwidth}% % \setlength{\@tempdimc}{.5\paperheight}% % \setlength{\unitlength}{1pt}% % \makebox(600,900){\rotatebox{45}{\textcolor[gray]{0.80}{\fontsize{5cm}{5cm}\selectfont{Draft v5}}}}} %\makeatother \newenvironment{smallitem} {\setlength{\topsep}{0pt} \setlength{\partopsep}{0pt} \setlength{\parskip}{0pt} \begin{itemize} \setlength{\leftmargin}{.2in} \setlength{\parsep}{0pt} \setlength{\parskip}{0pt} \setlength{\itemsep}{0pt}} {\end{itemize}} \newenvironment{smallenum} {\setlength{\topsep}{0pt} \setlength{\partopsep}{0pt} \setlength{\parskip}{0pt} \begin{enumerate} \setlength{\leftmargin}{.2in} \setlength{\parsep}{0pt} \setlength{\parskip}{0pt} \setlength{\itemsep}{0pt}} {\end{enumerate}} \definecolor{listinggray}{gray}{0.9} \singlespacing \begin{document} \thispagestyle{empty} \fig{css} generated as in the Lewis paper (using raw data with 1151 features): \bi \item the data is clustered to 3 clusters \item the mean is found for each cluster and plotted \ei \fig{fs} generated using fastmapped data of 4 features. The values of each feature are plotted. \begin{figure}[h!] \begin{center} \scalebox{0.97}{ \begin{tabular}{c} \resizebox{100mm}{!}{\includegraphics{C1}} \\ \resizebox{100mm}{!}{\includegraphics{C2}} \\ \resizebox{100mm}{!}{\includegraphics{C3}} \\ \end{tabular}} \caption{Infrared spectra of the mean of classes produced by kmeans clustering on 185 infrared spectra from 37 vehicles.} \label{fig:css} \end{center} \end{figure} \begin{figure}[h!] \begin{center} \scalebox{0.97}{ \begin{tabular}{c} \resizebox{92mm}{!}{\includegraphics{f1a}} \resizebox{92mm}{!}{\includegraphics{f2a}} \\ \resizebox{92mm}{!}{\includegraphics{f3a}} \resizebox{92mm}{!}{\includegraphics{f4a}} \\ \end{tabular}} \caption{FastMapped infrared spectra for 4 features.} \label{fig:fs} \end{center} \end{figure} \begin{figure} \begin{center} \begin{tabular}{l@{~}|l@{~}|c@{~}| c@{~}|} \cline{1-4} Clusters & n & Final Instances & Reduction\% \\\hline %\multirow{6}{*}{audio (226, 71, 24)} & n=1 & 36 & 19 \\ % & n=2 & 100 & 54 \\ % & n=4 & 100 & 54 \\ % & n=8 & 100 & 54 \\ % & n=16 & 100 & 54 \\ % \hline \multirow{6}{*}{bc (286, 10, 2)} & n=1 & 272 & 95 \\ & n=2 & 272 & 95 \\ & n=4 & 200 & 70 \\ & \colorbox{gray}{\color{white} n=7} & 58 & \colorbox{gray}{\color{white} 20} \\ & n=8 & 8 & 3 \\ & n=16 & 0 & 0 \\ \hline \multirow{6}{*}{heart (297, 14, 5)} & n=1 & 249 & 84 \\ & n=2 & 200 & 67 \\ & n=4 & 176 & 59 \\ & n=8 & 64 & 22 \\ & \colorbox{gray}{\color{white} n=9} & 35 & \colorbox{gray}{\color{white} 12} \\ & n=16 & 0 & 0 \\ \hline \multirow{6}{*}{lym (148, 19, 4)} & n=1 & 120 & 81 \\ & n=2 & 118 & 80 \\ & n=4 & 115 & 78 \\ & n=8 & 115 & 78 \\ & n=16 & 32 & 22 \\ & \colorbox{gray}{\color{white} n=17} & 20 & \colorbox{gray}{\color{white} 14} \\ \hline \multirow{5}{*}{pima (768, 9, 2)} & n=1 & 375 & 49 \\ & \colorbox{gray}{\color{white} n=2} & 95 & \colorbox{gray}{\color{white} 12} \\ & n=4 & 13 & 2 \\ & n=8 & 0 & 0 \\ & n=16 & 0 & 0 \\ \hline \multirow{6}{*}{tumor (339, 18, 21)} & n=1 & 270 & 80 \\ & n=2 & 255 & 75 \\ & n=4 & 255 & 75 \\ & n=8 & 255 & 75 \\ & n=16 & 193 & 57 \\ & \colorbox{gray}{\color{white} n=18} & 126 & \colorbox{gray}{\color{white} 37} \\ \hline %\multirow{6}{*}{splice (3190, 61, 3)} & n=1 & 36 & 19 \\ % & n=2 & 100 & 54 \\ % & n=4 & 100 & 54 \\ % & n=8 & 100 & 54 \\ % & n=16 & 100 & 54 \\ % \hline \end{tabular} \end{center} \caption{Instance selection using the CLIFF selector. The Reduction\% column shows the percentage of the original data set left after selection.}\label{fig:instances} \end{figure} \begin{figure}[h!] \begin{center} \scalebox{0.83}{ \begin{tabular}{c} \resizebox{73mm}{!}{\includegraphics{bc1-4}} \resizebox{73mm}{!}{\includegraphics{bc1-7}} \resizebox{73mm}{!}{\includegraphics{bc1-8}} \\ \resizebox{73mm}{!}{\includegraphics{heart1-4}} \resizebox{73mm}{!}{\includegraphics{heart1-8}} \resizebox{73mm}{!}{\includegraphics{heart1-9}} \\ \resizebox{73mm}{!}{\includegraphics{lym1-8}} \resizebox{73mm}{!}{\includegraphics{lym1-16}} \resizebox{73mm}{!}{\includegraphics{lym1-17}} \\ \resizebox{73mm}{!}{\includegraphics{pima1-1}} \resizebox{73mm}{!}{\includegraphics{pima1-2}} \resizebox{73mm}{!}{\includegraphics{pima1-4}} \\ \end{tabular}} \caption{Breast Cancer, Heart, Lymph, Pima.} \label{fig:css} \end{center} \end{figure} \begin{figure}[h!] \begin{center} \scalebox{0.83}{ \begin{tabular}{c} \resizebox{100mm}{!}{\includegraphics{bcfss-4}} \resizebox{100mm}{!}{\includegraphics{heartfss-3}} \\ \resizebox{100mm}{!}{\includegraphics{lym10fss-10}} \resizebox{100mm}{!}{\includegraphics{pimafss-2}} \\ \end{tabular}} \caption{Breast Cancer, Heart, Lymph, Pima.} \label{fig:fss} \end{center} \end{figure} \begin{figure} \begin{center} \begin{tabular}{l@{~}|l@{~}|c@{~}| c@{~}|} \cline{1-4} Clusters & n & Final Instances & Reduction\% \\\hline %\multirow{6}{*}{audio (226, 71, 24)} & n=1 & 36 & 19 \\ % & n=2 & 100 & 54 \\ % & n=4 & 100 & 54 \\ % & n=8 & 100 & 54 \\ % & n=16 & 100 & 54 \\ % \hline \multirow{4}{*}{bc (286, 10, 2)} & n=1 & 55 & 19 \\ & n=2 & 55 & 19 \\ & n=3 & 50 & 17 \\ & \colorbox{gray}{\color{white} n=4} & 24 & \colorbox{gray}{\color{white} 8} \\ \hline \multirow{4}{*}{heart (297, 14, 5)} & n=1 & 79 & 27 \\ & n=2 & 52 & 18 \\ & \colorbox{gray}{\color{white} n=3} & 30 & \colorbox{gray}{\color{white} 10} \\ & n=4 & 22 & 7 \\ \hline \multirow{1}{*}{lym (148, 19, 4)} & n=10 & 65 & 44 \\ % & n=2 & 118 & 80 \\ % & n=4 & 115 & 78 \\ % & n=8 & 115 & 78 \\ % & n=16 & 32 & 22 \\ % & \colorbox{gray}{\color{white} n=17} & 20 & \colorbox{gray}{\color{white} 14} \\ \hline \multirow{4}{*}{pima (768, 9, 2)} & n=1 & 362 & 47 \\ & \colorbox{gray}{\color{white} n=2} & 92 & \colorbox{gray}{\color{white} 12} \\ & n=3 & 36 & 5 \\ & n=4 & 11 & 1 \\ \hline %\multirow{6}{*}{tumor (339, 18, 21)} & n=1 & 270 & 80 \\ %& n=2 & 255 & 75 \\ %& n=4 & 255 & 75 \\ %& n=8 & 255 & 75 \\ %& n=16 & 193 & 57 \\ % & \colorbox{gray}{\color{white} n=18} & 126 & \colorbox{gray}{\color{white} 37} \\ %\hline %\multirow{6}{*}{splice (3190, 61, 3)} & n=1 & 36 & 19 \\ % & n=2 & 100 & 54 \\ % & n=4 & 100 & 54 \\ % & n=8 & 100 & 54 \\ % & n=16 & 100 & 54 \\ % \hline \end{tabular} \end{center} \caption{Instance selection using the CLIFF selector. The Reduction\% column shows the percentage of the original data set left after selection.}\label{fig:fssinstances} \end{figure} \begin{verbatim} ---------------------------------------------- -------------CBR vs FSS+CBR------------------- ---------------------------------------------- Before After Breast Cancer pd pf pd pf CBR 61 40 60 40 FSS+CBR 56 44 70 30 ---------------------------------------------- Before After Heart pd pf pd pf CBR 22 12 0 4 FSS+CBR 33 13 38 10 ---------------------------------------------- Before After Lymph pd pf pd pf CBR 70 0 59 0 FSS+CBR 60 8 50 3 ---------------------------------------------- Before After Pima Diabetes pd pf pd pf CBR 58 42 61 39 FSS+CBR 58 42 58 42 ---------------------------------------------- \end{verbatim} \begin{verbatim} -------------------------------------------------- --------------Experiment 1 - BestK---------------- -------------------------------------------------- Dataset bestK Breast Cancer 3 Heart 1 Lymph 1 Pima Diabetes 3 ------------------------------------------------- Naive Bayes Dataset pd pf Breast Cancer 100 0 Heart 22 8 Lymph 100 0 Pima Diabetes 100 0 -------------------------------------------------- ---Experiment 2 - Within System Brittle Check----- -------------------------------------------------- NB. Definition of brittleness if NUN >= NLN then brittle = yes \end{verbatim} \begin{center} \begin{tabular}{ p{8cm} | p{8cm} } Before & After \\\hline \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Breast Cancer 61 40 yes (7, 20%) #key, ties, win, loss, win-loss NUN, 0, 1, 0, 1 NLN, 0, 0, 1, -1 ----------------------------------- \end{verbatim} & \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Breast Cancer 60 40 no (7, 20%) #key, ties, win, loss, win-loss NLN, 0, 1, 0, 1 NUN, 0, 0, 1, -1 ----------------------------------- \end{verbatim} \\\hline \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Heart 22 12 yes (9, 12%) #key, ties, win, loss, win-loss NUN, 1, 0, 0, 0 NLN, 1, 0, 0, 0 ----------------------------------- \end{verbatim} & \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Heart 0 4 yes (9, 12%) #key, ties, win, loss, win-loss NLN, 1, 0, 0, 0 NUN, 1, 0, 0, 0 ----------------------------------- \end{verbatim} \\\hline \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Lymph 70 0 yes (17, 14%) #key, ties, win, loss, win-loss NUN, 1, 0, 0, 0 NLN, 1, 0, 0, 0 ----------------------------------- \end{verbatim} & \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Lymph 59 0 no (17, 14%) #key, ties, win, loss, win-loss NLN, 0, 1, 0, 1 NUN, 0, 0, 1, -1 ----------------------------------- \end{verbatim} \\\hline \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Pima Diabetes 58 42 yes (2, 12%) #key, ties, win, loss, win-loss NUN, 1, 0, 0, 0 NLN, 1, 0, 0, 0 ----------------------------------- \end{verbatim} & \begin{verbatim} ----------------------------------- Dataset pd pf brittle? ----------------------------------- Pima Diabetes 61 39 yes (2, 12%) #key, ties, win, loss, win-loss NUN, 1, 0, 0, 0 NLN, 1, 0, 0, 0 ----------------------------------- \end{verbatim} \\\hline \end{tabular} \end{center} \end{document}