TABLE 2 - uploaded by Sherry Y. Chen
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Wrapper feature selection approaches are widely used to select a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier when selecting the features. The downside to this approach is that each classifier will have its own biases and will therefore select very different features...
Contexts in source publication
Context 1
... is based on the BN family, another on the DT family and the other on the KNN family. For each family, three classifiers of the same nature are used which are showed in Table 2. The reason for choosing and combining these classifiers to do the feature selection is two- fold. ...
Context 2
... reason for choosing and combining these classifiers to do the feature selection is two- fold. First, previous studies have showed that the three classifiers which belong to each family in Table 2 produce very good performance when used to classify user preference data, e.g. [16] [17]. ...
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