Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Feature selection has been an important issue in machine learning and data mining, and is
unavoidable when confronting with high‐dimensional data. With the advent of multilabel …

Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …

Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the
top 10 algorithms in the data mining and machine learning community. Of numerous …

In recent years, botnets have become a major threat on the Internet. Most sophisticated bots
use Domain Generation Algorithms (DGA) to pseudo-randomly generate a large number of …

In recent years, the number and variety of malicious mobile apps have increased drastically,
especially on Android platform, which brings insurmountable challenges for malicious app …

This paper tackles the issue of objective performance evaluation of machine learning
classifiers, and the impact of the choice of test instances. Given that statistical properties or …

The class imbalance issue has been a persistent problem in machine learning that hinders
the accurate predictive analysis of data in many real-world applications. The class …