Sentence examples for imbalanced learning from inspiring English sources

The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering.

Multi-class imbalanced learning problems are also very popular and very difficult to solve in medical decision making.

In contrast to sampling methods and cost-sensitive methods that are specially designed to address imbalanced learning problem, boosting is an off-the-shelf approach that is particularly effective in handling imbalanced data.

In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective.

Last, the risk score is fine-tuned through a novel imbalanced learning strategy.

Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem.

An internal (Biased Fuzzy SVM) and external (data under-sampling) class imbalance learning methods were applied to the data.

It is therefore concluded that the weighted rough set based method is effective for class imbalance learning.

The proposed methodology is applied on 22 UCI data sets and experimental results indicate a significant increase in performance when compared with many existing class-imbalance learning methods.

Furthermore, in order to estimate the performance of the developed method, we compare the weighted rough set based method with several popular methods used for class imbalance learning by conducting experiments with twenty UCI data sets.

Multi-label learning and imbalance learning techniques are necessary to process the medical data sets with above problems.