Consistent batch normalization for weighted loss in imbalanced-data environment

Minimizing Total Weighted Tardiness in a Flexible Flowshop Environment Considering Batch Processing Machines

Scheduling in production environments is used as a competitive tool to improve efficiency and respond to customer requests. In this paper, a scheduling problem is investigated in a three-stage flexible flowshop environment with the consideration of blocking and batch processing. This problem has been inspired by the charging and packaging line of a large battery manufacturer. In this environmen...

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

In this paper, a novel k -nearest neighbors (kNN) weighting strategy is proposed for handling the problem of class imbalance. When dealing with highly imbalanced data, a salient drawback of existing kNN algorithms is that the class with more frequent samples tends to dominate the neighborhood of a test instance in spite of distance measurements, which leads to suboptimal classification performa...

On Mining Fuzzy Classification Rules for Imbalanced Data

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

cDNA Microarray Data Normalization

Recurrent Batch Normalization

We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evalua...