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In addition, current research has ignored the use of ensemble learning techniques, such Bagging or Boosting, to obtain better predictive performance than using the traditional learning algorithms.
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As known, the margin theory is another important means that can be used to explain the effectiveness of ensemble learning.
Ensemble margin, computed as the difference of the vote numbers received by the correct class and the another class received with the most votes, is widely used to explain the success of ensemble learning.
Random forests (RF) are a type of ensemble learning.
The results confirm the utility of ensemble learning to identify ABC transporters substrates and nonsubstrates.
The application of ensemble learning techniques reveals the most informative features in discriminating oncogenic gene fusions.
It can also be used as a form of ensemble learning by incorporating predictions by other independent tools as features.
It is worth noting that in the conventional SEC framework, only one stage of ensemble learning is used to integrate the classification results of different frames in one sound file.
FAUST [ 72] is distinct from TEES and EventMine in its usage of a stacking technique (a type of ensemble learning technique, i.e. a way of combining models rather than using a single model).
Techniques from the field of ensemble learning may prove useful in this context (Polikar, 2006).
The generalization error of ensemble learning E can be calculated by using the following equation: (1) E = E ¯ − A ¯, where E ¯ and A ¯ are averages of generalization errors and diversities, respectively.
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