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authorTimotej Lazar <timotej.lazar@fri.uni-lj.si>2017-04-16 15:47:27 +0200
committerTimotej Lazar <timotej.lazar@fri.uni-lj.si>2017-04-16 15:47:27 +0200
commit1c8228f891a04fcb9d66ac5ffc4a6291f662c330 (patch)
treeca73b5c85acebbc289986e3d2583a69689e22ba2
parent4d161f0c8307a803ea023fb566dc1c772aa99d6b (diff)
Fix paper URL
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@@ -35,7 +35,7 @@ The bottom two rows give aggregated results (total and average) over all 44 doma
\label{table:eval}
\end{table}
-Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/ast-patterns/}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method,
+Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/aied2017}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method,
which had the best overall performance, serve as references for bad and good CA on particular data sets.
For example, our rules correctly classified 99\% of testing instances for the \code{sister} problem,