Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Novelty detection is especially important for monitoring safety-critical systems in which novel
conditions rarely occur and knowledge about novelty in that system is often limited or …

This paper presents a new approach to classification of high-dimensional spectroscopy data
and demonstrates that it outperforms other current state-of-the art approaches. The specific …

Web spam has the effect of polluting search engine results and decreasing the usefulness of
search engines. Web spam can be classified according to the methods used to raise the …

Electroencephalography (EEG) signal has been used widely in health and medical fields. It
is also used in brain-computer interface (BCI) systems for humans to continuously control …

This paper presents a level set boundary description (LSBD) approach for novelty detection
that treats the nonlinear boundary directly in the input space. The proposed approach …

Software vulnerabilities (SVs) have become a common, serious, and crucial concern due to
the ubiquity of computer software. Many AI-based approaches have been proposed to solve …

Software vulnerabilities (SVs) have become a common, serious and crucial concern due to
the ubiquity of computer software. Many machine learning-based approaches have been …

Multi-metric learning is important for improving performance of learners. For complex data,
multi metric learning algorithms need intensive research. Moreover, the existing multi-metric …

In support vector data description (SVDD) a spherically shaped boundary around a normal
data set is used to separate this set from abnormal data. The volume of this data description …