Sentence examples for majority classes are from inspiring English sources

It is interesting to notice that farthest-first beats d-Confidence on the majority classes (Tables 10) but, when all majority classes are found and only minority classes are left unexposed, d-Confidence reveals its ability to find rare instances.

The complete database, composed of the minority and majority classes, is grouped into k clusters.

The examples of the majority class are selected randomly and the examples of the minority class are sparely distributed.

The most straightforward method is under-sampling, in which instances that belong to the majority class are eliminated until a balanced distribution is reached.

From these clusters, samples of the majority class are selected according to the proportion of samples of this class (SizeMA) and the minority class (SizeMI) in each cluster i.

However, from 500m (actually 463.3m) to 1km, the majority class is very difficult to determine in the 1km grid box, as shown in the figure below.

In these cases, the problem is that a bias towards the majority class is shown during the learning process, seeking for both accuracy and generalization.

When comparing similar strategies across cost factors, we see that the cost factor of 1 incurs larger costs overall, since the majority class is penalized more than its counterpart, and thus by sheer abundance, drives up the NPV costs.

To avoid the problems caused by imbalance in the training set (i.e. many more inactives than actives) for RFs, the balanced random forest algorithm [26] was applied: for each decision tree the majority class is down-sampled to yield an equal number of instances as the minority class.

Leaves with the same majority class were recursively merged with an m-value of two.

In our case, the majority class is the negative class, which implies a low sensitivity (true positive rate).