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Automated Feature Weighting in Naive Bayes for High-dimensional Data Classification| old_uid | 13142 |
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| title | Automated Feature Weighting in Naive Bayes for High-dimensional Data Classification |
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| start_date | 2013/12/05 |
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| schedule | 10h |
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| online | no |
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| location_info | 25-26, salle 105 |
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| summary | Granular models based on fuzzy clustering are presented as an approach for
time series forecasting. These models are constructed in two phases.
The first one uses the clustering algorithms to find group structures in a
historical database. Two different approaches are discussed: fuzzy c-means
clustering and participatory learning algorithms. Fuzzy c-mean clustering,
which is a supervised clustering algorithm, is used to explore similar
data characteristics, such as trend or cyclical components. Participatory
learning induces unsupervised dynamic fuzzy clustering algorithms and
provides an effective alternative to construct adaptive fuzzy systems.
In the second phase, two cases are considered. In the first case, a
regression model is adjusted for each cluster and forecasts are produced
by a weighted combination of the local regression models. In the second
case, prediction data are classified according to the group structure
found in the database. Then, forecasts are produced using the cluster
centers weighted by the degree with which prediction data match the
groups. The weighted combination of local models constitutes a forecasting
approach called granular functional forecasting modeling, and the approach
based on weighted combination cluster centers comprises granular
relational forecasting modeling. The effectiveness of the granular
forecasting approaches is verified using three different applications:
average streamflow forecasting, pricing option estimation and modeling of
regime changes in Brazilian nominal interest rates. |
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| responsibles | Piwowarski |
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