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move subsection effect of stored configurations

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hannes.kuchelmeister
2020-04-06 11:28:13 +02:00
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30_Thesis/sections/60_evaluation.tex
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@@ -95,10 +95,6 @@ Unfinished configurations are generated using all finished configurations and ta
For the forest use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude towards a characteristic. During data generation the attitude is converted to a preference \todo{hier evlt nochmal nennen, dass du Präferenzen zwischen 0 und 1 verwendest, steht aktuell nur in der Grafik} using a normal distribution. \autoref{fig:Evaluation:DataGeneration} shows how the user profile can be converted to preferences. The actual group member profiles are shown in \autoref{tab:Evaluation:GroupMemberMappings}. For the forest use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude towards a characteristic. During data generation the attitude is converted to a preference \todo{hier evlt nochmal nennen, dass du Präferenzen zwischen 0 und 1 verwendest, steht aktuell nur in der Grafik} using a normal distribution. \autoref{fig:Evaluation:DataGeneration} shows how the user profile can be converted to preferences. The actual group member profiles are shown in \autoref{tab:Evaluation:GroupMemberMappings}.
\subsection{The Effect of Stored Finished Configurations}
When evaluating a subset of stored finished configurations it is important to avoid outliers. This is the reason why a process inspired by \emph{cross validation} \todo{referenz hinzufügen} is used. The configuration database is randomly ordered and sliced into sub databases of the needed size. As an example, if the evaluated stored data size is 20, a configuration database containing 100 configurations is split into five sub databases of size 20. Now the evaluation is done on each of the sub databases and as a result the average is taken. This avoid that randomly a subset can be picked which either performs much better than most other possible combinations of databases or which performs much worse. This way the data is more aligned to the \emph{expected value} \todo{referenz}.
\pgfplotsset{height=5cm,width=\textwidth,compat=1.8} \pgfplotsset{height=5cm,width=\textwidth,compat=1.8}
\pgfmathdeclarefunction{gauss}{2}{% \pgfmathdeclarefunction{gauss}{2}{%
\pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}% \pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}%
@@ -175,6 +171,11 @@ These user profiles can be used to generate rather homogenous groups but also to
\end{center} \end{center}
\end{table} \end{table}
\subsection{The Effect of Stored Finished Configurations}
When evaluating a subset of stored finished configurations it is important to avoid outliers. This is the reason why a process inspired by \emph{cross validation} \todo{referenz hinzufügen} is used. The configuration database is randomly ordered and sliced into sub databases of the needed size. As an example, if the evaluated stored data size is 20, a configuration database containing 100 configurations is split into five sub databases of size 20. Now the evaluation is done on each of the sub databases and as a result the average is taken. This avoid that randomly a subset can be picked which either performs much better than most other possible combinations of databases or which performs much worse. This way the data is more aligned to the \emph{expected value} \todo{referenz}.
\section{Hypotheses} \section{Hypotheses}
\label{sec:Evaluation:Hypotheses} \label{sec:Evaluation:Hypotheses}