The past decade has seen a significant interest on the problem of inducing decision trees
that take account of costs of misclassification and costs of acquiring the features used for …

Classifier ensembles have been utilized in the industrial cybersecurity sector for many years.
However, their efficacy and reliability for intrusion detection systems remain questionable in …

Void defects seriously threaten the overall force of the Sandwich-structured immersed tunnel
(SSIT). Accurately identifying the location and evaluating the severity of the void defect …

In classification, an algorithm learns to classify a given instance based on a set of observed
attribute values. In many real world cases testing the value of an attribute incurs a cost …

In classification, an algorithm learns to classify a given instance based on a set of observed
attribute values. In many real world cases testing the value of an attribute incurs a cost …

This paper develops a new algorithm for inducing cost-sensitive decision trees that is
inspired by the multi-armed bandit problem, in which a player in a casino has to decide …

Tree-based and rule-based machine learning models play pivotal roles in explainable
artificial intelligence (XAI) due to their unique ability to provide explanations in the form of …

The concept and effects of gravitation have been effectively utilized to design various data
classification algorithms. Generally, there are two primary approaches to gravitation‐based …

The current research investigates a single cost for cost-sensitive neural networks (CNN) for
decision making. This may not be feasible for real cost-sensitive decisions which involve …

Classification is a data mining technique used to predict group membership for data
instances. Cost-sensitive classifier is relatively new field of research in the data mining and …