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This indicates that contributing more individual classifiers will make the made-off MCS more efficient.
The system is based on integrating two or more individual classifiers.
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The ensembles are quite often more accurate than the individual classifiers that make them up.
In this sense, it is more general than typical individual classifiers and unit words.
This and the consistent reduction of the standard deviation of the performance measures lead to the conclusion that the mean combiner is more robust than the individual classifiers, although the performance is not always significantly improved.
This is an expected result given the fact that increasing the number of individual classifiers will makes more chances of these classifiers first to give different results and second to produce uncorrelated errors,[14].
However, the term 'type classifier' is probably an even more appropriate name as they differ from individual classifiers by selecting types instead of tokens (i.e. individuals).
The use of meta-classifiers is a more sophisticated method of classifier combination, which uses the individual classifiers' outputs as input for a second classification step.
In order to provide a more reliable and robust recommendation several methods for combining the individual classifiers were investigated.
In the future, we intend to consider more elaborate semantic classes for features and also consider training individual classifiers for each category which we would then combine using stacking or ensemble techniques.
As stated in [ 21], a necessary and sufficient condition for an ensemble to be more accurate than any of its constituent classifiers is when the individual classifiers have predictive power (better than random), and are diverse (they make different errors).
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