Machine Learning and Data Mining research strongly depend on the quality and quantity of the real world datasets for the evaluation stages of the developing methods. In the context of the emerging Online Multi-Target Regression and Multi-Label Classification methodologies, datasets present new characteristics that require specific testing and represent new challenges. The first difficulty found...

Packet assembly at the network edge is one solution to reduce high packet rates in core network switches. Literature discusses this topic controversially because of three reasons: (1) potential negative impact of packet assembly on the traffic characteristics, (2) disruptive integration into existing networks and (3) lack of support of packet assembly in existing control plane environment. In t...

In many real data mining tasks, one data object is often associated with multiple class labels simultaneously; for example, a document may belong to multiple topics, an image can be tagged with multiple terms, etc. Multi-label learning focuses on such problems, and it is well accepted that the exploitation of relationship among labels is crucial; actually this is the essential difference betwee...

We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evaluate modularity-maximizing using fast greedy and leading eigenvector approximations, infomap, walktrap and label propagation algorithms. For this pur...

Multi-Instance Multi-Label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a ...

In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set for each unseen instance. This paper describes a new method for multi-label classification based on the Dempster-Shafer theory of belief functions to classify an unseen instance on the basis of its k nearest neighbors. The proposed method generalizes an existing s...

In many real data mining tasks, one data object is often associated with multiple class labels simultaneously; for example, a document may belong to multiple topics, an image can be tagged with multiple terms, etc. Multi-label learning focuses on such problems, and it is well accepted that the exploitation of relationship among labels is crucial; actually this is the essential difference betwee...

In multi-label learning, each instance can be related with one or more binary target variables. Multi-label learning problems are commonly found in many applications, e.g. in text classification where a news article is possible to be both on politics and finance. The main motivation of multi-label learning algorithms is the exploitation of label dependencies in order to improve prediction accur...

The goal of multi-label classification is to predict multiple labels per data point simultaneously. Real-world applications tend to have high-dimensional label spaces, employing hundreds or even thousands of labels. While these labels could be predicted separately, by capturing label correlation we might achieve better predictive performance. In contrast with previous attempts in the literature...