Classification

Time series feature learning with labeled and unlabeled data

Time series classification has attracted much attention in the last two decades. However, in many real-world applications, the acquisition of sufficient amounts of labeled training data is costly, while unlabeled data is usually easily to be …

Multi-instance learning with discriminative bag mapping

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 a single instance in a new space via instance selection and has …

Positive and unlabeled multi-graph learning

In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented as a bag of graphs and the label is only available to each bag but not individual graphs. In addition, when …

SODE: Self-adaptive one-dependence estimators for classification

SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute independence assumption of Naive Bayes (NB) to allow each attribute to depend on a common single attribute (superparent). …

Finding the best not the most: regularized loss minimization subgraph selection for graph classification

Classification on structure data, such as graphs, has drawn wide interest in recent years. Due to the lack of explicit features to represent graphs for training classification models, extensive studies have been focused on extracting the most …

Locally weighted learning: how and when does it work in Bayesian networks?

Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes …

Ensemble of multiple descriptors for automatic image annotation

Automatic image annotation (AIA) plays an important role and attracts much research attention in image understanding and retrieval. Annotation can be posed as classification problems where each annotation keyword is defined as a group of database …