Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing increases. Changes in distribution, also known concept drift, cause existing classification models lose their effectiveness. To assist classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an unsupervised drift detector using label dependencies w...

Image and video annotations are challenging but important tasks to understand digital multimedia contents in computer vision, which by nature is a multi-label multi-class classification problem because every image is usually associated with more than one semantic keyword. As a result, label assignments are no longer confined to class membership indications as in traditional single-label multi-c...

In many application domains, such as machine learning, scene and video classification, data mining, medical diagnosis and machine vision, instances belong to more than one categories. Feature selection in single label text classification is used to reduce the dimensionality of datasets by filtering out irrelevant and redundant features. The process of dimensionality reduction in multi-label cla...

Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. This paper proposes a new experimental framework for studying multi-label...

Stratified sampling is a sampling method that takes into account the existence of disjoint groups within a population and produces samples where the proportion of these groups is maintained. In single-label classification tasks, groups are differentiated based on the value of the target variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear ...

In big data problems mining requires special handling of the problem under investigation to achieve accuracy and speed on the same time. In this research we investigate the multi-label classification problems for better accuracy in a timely fashion. Label dependencies are the biggest influencing factor on performance, directly and indirectly, and is a distinguishing factor for multi-label from ...

Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal wi...

Many existing researches employ one-vs-others approach to decompose a multi-label classification problem into a set of 2-class classification problems, one for each class. This approach is valid in traditional single-label classification. However, it incurs training inconsistency in multi-label classification, because a multi-label data point could belong to more than one class. In this work, w...

The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analyse data from mutant phenotype growth experiments with the yeast S. cerevisiae to predict novel gene functions. The analysis of the data presented a number of challenges: multi-class labels, a large number of sparsely p...