Sentence examples for imbalanced training from inspiring English sources

Imbalance In classification problems, imbalanced training data (when there are many more instances in one or more classes than in others) can lead to weak learners.

The cascade consists of consecutive independent RF classifiers with low to high posterior probability constraints to handle imbalanced training sets (CMBs and non-CMBs), and to progressively improve detection rates.

However, LDM generally has imbalanced margin distribution between two classes on imbalanced training data.

Simultaneously a Random Forest tree of the imbalanced training set is formed.

These assumptions are violated in most real-world applications, especially when classifiers are designed using limited and imbalanced training data.

This paper proposes a new method to design a balanced classifier on imbalanced training data based on margin distribution theory.

To study the effectiveness of the proposed loss functions, we have deliberately imbalanced the training samples in the satellite image problem and compared the performance of our neural classifiers with those developed using other well-known loss functions.

In a machine learning problem in which the test data are class-imbalanced, training is commonly done on class-balanced data, and class priors are then used to correct for the imbalance.

Using a balanced training set has an advantage in that some classifiers trained on balanced data often have better performance (as measured by ROC analysis) than classifiers trained on imbalanced data.

Algorithms trained from imbalanced data will often time favor the majority class producing models which may be incapable of predicting the minority class.

Respondents repeatedly cited imbalances in training as additional funding for disease-specific trainings diverted scarce administrative resources from trainings in other crucial support sectors.