Multi-label learning deals with training instances associated with multiple labels. Many common multi-label algorithms are to treat each label in a crisp manner, being either relevant or irrelevant to an instance, and such label can be called logical label. In contrast, we assume that there is a vector of numerical label behind each multi-label instance, and the numerical label can be treated a...

Multi-label classification is one of the important research areas in data mining. In this paper, a new multilabel classification method using multinomial naive Bayes is proposed. We use a new fine-grained weighting method for calculating the weights of feature values in multinomial naive Bayes. Our experiments show that the value weighting method could improve the performance of multinomial nai...

We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples. A label-only sample contains the label information of the sample but not the feature information. Then, we propose a simple and effective graph-based method for bidirectional semisupervised learning in multi-label classification. ...

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically derive useful constraints from data. In this paper, we experiment with two constraint-based correction approaches as p...

The objective of this position paper is to show that the integration of semantic data mining into the DAMIART data mining system can help further improve classification performance and knowledge extraction. DAMIART performs multi-label classification in the presence of multiple class ontologies, hierarchy extraction from multi-labels and concept relation by association rule mining. Whereas DAMI...

We extend the concept of generalized roof duality from pseudo-boolean functions to real-valued functions over multi-label variables. In particular, we prove that an analogue of the persistency property holds for energies of any order with any number of linearly ordered labels. Moreover, we show how the optimal submodular relaxation can be constructed in the first-order case.

The straightforward approach to multi-label classification is based on decomposition, which essentially treats all labels independently and ignores interactions between labels. We propose to enhance multilabel classifiers with features constructed from local patterns representing explicitly such interdependencies. An Exceptional Model Mining instance is employed to find local patterns represent...

GMPLS provides standardized protocols through which nodes can request and establish (or release) lightpaths on demand between themselves and peer nodes. The primary intent is to support automated provisioning for dynamic demand environments. But an apparently tempting assumption is that GMPLS therefore also provides a mechanism for physical layer network restoration, wherein all effected node p...

In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subcl...