Feature Selection (FS) methods alleviate key problems in classification procedures as they
are used to improve classification accuracy, reduce data dimensionality, and remove …

This paper will address the Positive and Unlabeled learning problem (PU learning) and its
importance in the growing field of semi-supervised learning. In most real-world classification …

To ensure the reliability of software services, operators collect and monitor a large number of
KPI (Key Performance Indicator) streams constantly. KPI anomaly detection is vitally …

Bayesian network classifiers (BNCs) are powerful tools for graphically encoding the
dependency relationships among variables in a directed acyclic graph and reasoning under …

Wildfire is one of the most common natural hazards in the world. Fire risk estimation for the
purposes of risk reduction is an important aspect in disaster studies around the world. The …

Traditional recommender systems rely on user profiling based on either user ratings or
reviews through bi-sentimental analysis. However, in real-world scenarios, there are two …

Positive and unlabeled learning (PU learning) is designed to solve the problem that we only
utilize the labeled positive examples and the unlabeled examples to train a classifier. A …

Textual data streams have been extensively used in practical applications where consumers
of online products have expressed their views regarding online products. Due to changes in …

Abstract Multiple Instance Learning (MIL) is a widely studied learning paradigm which arises
from real applications. Existing MIL methods have achieved prominent performances under …

Designing new materials with desired properties is one of the main challenges for the
current industrial and academic research, in the attempt to cover the societal demands. The …