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z-<(a2v4n#MI~?Q=!uC^y-Q*yOzeRkY=paqMMWDCvUkialFeE5D$m?%46ev0jIW`coHN&~PK16~bcwhoNPzc*kMD{<1pSzQ(88H;X z4?iQ$$w-M&=}Ow$bf6n{73Mxdgvp@ zfbmE2V+ZGvjnPLv0rkKEa#Rl{a5U6VC<<(-zx6;F8hbSqs2WD1m>gmH%kg!*!xL2J2QL8!I6S}AqmkDHb4vTBM~quahSNY6jDG6 xAr4MvaHzwj`B4bC1R8Z2BK%)fbRpnb>5RP{IyfU>Fn&0ckC>HJN=cgde*lP$BE|p! diff --git a/term-paper/title.tex b/term-paper/title.tex index 69c4635..007a010 100644 --- a/term-paper/title.tex +++ b/term-paper/title.tex @@ -12,5 +12,8 @@ \maketitle \begin{abstract} -Folgt +Diese Arbeit untersucht die Zuverlässigkeit der Aktivitätserkennung anhand von Smartphone-Sensordaten des UCI Human Activity Recognition Datasets.\\ +Nach einer Feature-Selection mittels Korrelationsanalyse wurden drei Klassifikationsverfahren (Decision Tree, KNN und Random Forest) trainiert und anhand des F1-Scores bewertet.\\ +Random Forest und KNN lieferten durchschnittliche Ergebnisse, während der Decision Tree signifikant schlechter war. +Damit zeigt sich, dass eine grundlegende Aktivitätserkennung möglich ist, jedoch noch Optimierungspotenzial hinsichtlich Modellwahl und Datenaufbereitung besteht. \end{abstract} \ No newline at end of file