Sentence examples for classification imbalance from inspiring English sources

However, the fact that the treatment effect remained significant when analyzed using time to event survival methodology, which accounts for dropouts, as well as when adjusting for Child-Pugh classification imbalance between study arms, suggests a statistically robust treatment effect of GPB.

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.

In [53] authors developed a data mining process for classification of imbalance data based on MapReduce to predict traffic accidents with the highway traffic data.

High dimensionality and classification of imbalanced data sets are two of the most interesting machine learning challenges.

In the two approaches considered, the use of distributed SVM architectures, which basically reduces the complexity of the quadratic optimization problem of very large data sets, and the use of two sampling approaches for classification of imbalanced data sets were combined and results presented.

The results could support planning and operations decisions for re-design and management of BSSs in relation to the spatial implications of the stations and the users' mobility patterns, due to the classification reveals imbalances in the distribution of bikes and lead to a better understanding of the system structure.

Classification of imbalanced data sets is recognized by numerous available techniques working at dissimilar levels.

The ordinal classification of imbalanced Big Data sets in [31], approximates the class probability distribution using the weighted KNN technique.

In this work, we present a novel boosting algorithm for the classification of imbalanced data.

Transductive approaches demonstrated great potential for protein classification from imbalanced datasets [ 32], and SVM has previously been shown to successfully identify splice sites [ 18].

For the classification of imbalanced data, accuracy is not an appropriate measure because it may be still high when the sensitivity is very low [ 65].