As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …

With the continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

Currently, the different deep neural network (DNN) learning approaches have done much for
the classification of hyperspectral images (HSIs), especially most of them use the …

Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

Under sampling is a popular technique for unbalanced datasets to reduce the skew in class
distributions. However, it is well-known that under sampling one class modifies the priors of …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

Imbalanced learning problems contain an unequal distribution of data samples among
different classes and pose a challenge to any classifier as it becomes hard to learn the …

In this paper, we take up the second RE17 data challenge: the identification of requirements
types using the" Quality attributes (NFR)" dataset provided. We studied how accurately we …

Class imbalance problem is quite pervasive in our nowadays human practice. This problem
basically refers to the skewness in the data underlying distribution which, in turn, imposes …