Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity,
high computational efficiency, and good classification accuracy, especially for high …

Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning
because it allows a bag of instances to share one label. Bag mapping transforms a bag into …

In this paper, we formulate a novel graph-based learning problem, multi-graph classification
(MGC), which aims to learn a classifier from a set of labeled bags each containing a number …

Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To
date, all existing MTL methods have been designed for tasks with feature-vector represented …

This paper introduces the Bag of Graphs (BoG), a Bag-of-Words model that encodes in
graphs the local structures of a digital object. We present a formal definition, introducing …

Many applications involve stream data with structural dependency, graph representations,
and continuously increasing volumes. For these applications, it is very common that their …

Graph classification aims to learn models to classify structure data. To date, all existing
graph classification methods are designed to target one single learning task and require a …

Abstract SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-
naive Bayesian classifiers which relax the attribute independence assumption of Naive …