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Table 1: Parameters that generated the best F1-Score for each evaluated classifier.
kNN
proposed
kNN
traditional
kNN
dist
XGBoost
Vector Dimensionality 700 800 1200 800
K 37 2 4 -
Imbalace Factor 0.29 - - -
Table 2: Results obtained for each of the techniques evaluated.
kNN
proposed
kNN
traditional
kNN
dist
XGBoost
F1-Score 0.55 ? 0.44 0.44 0.44
Accuracy 0.93 0.95 0.93 0.93
Precision 0.57 0.75 0.66 0.75
Recall 0.52 0.31 0.33 0.31
However, in general, the performance in the minority
class is the most important to consider, especially for
the application scenario of this work.
The approach proposed in this work considered
two important aspects when predicting the approval
of LPs: (i) the database imbalance factor and (ii) the
time in which proposals were submitted. These two
aspects were mitigated by modifying the traditional
kNN algorithm: we increased the distance between
disapproved documents and those that were further
away in time. Experimental results showed that the
two modifications proposed in the kNN algorithm
allowed to increase the classifier’s performance. The
proposed technique obtained a high recall rate among
the evaluated techniques, showing its potential in
predicting propositions with high chances of approval
in legislative houses, contributing with a valuable
tool to be used in the legislative process.
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