Nosocomial infections (NIs) - those acquired in health care settings - represent one of the major causes of increased mortality in hospitalized patients. As they are a real problem for both patients and health authorities, the development of an effective surveillance system to monitor and detect them is of paramount importance. This paper presents a retrospective analysis of a prevalence survey...

Several problems in computational biology and medicine are modelled as learning problems in graphs, where nodes represent the biological entities to be studied, e.g. proteins, and connections different kinds of relationships among them, e.g. protein-protein interactions. In this context, classes are usually characterized by a high imbalance, i.e. positive examples for a class are much less than...

Named entity recognition is a fundamental task in biomedical data mining. Multiple -class annotation is more challenging than single class annotation. In this paper, we took a single word classification approach to dealing with the multiple -class annotation problem using Support Vector Machines (SVMs). Word attributes, results of existing gene/protein name taggers, context, and other informati...

One problem of data-driven answer extraction in open-domain factoid question answering is that the class distribution of labeled training data is fairly imbalanced. This imbalance has a deteriorating effect on the performance of resulting classifiers. In this paper, we propose a method to tackle class imbalance by applying some form of cost-sensitive learning which is preferable to sampling. We...

Recently, Class imbalance problems have growing interest because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble learning and machine learning methods have been proposed for classification of imbalance problem. However, these methods producing poor predictive accuracy of classification for two-class imbalanced dataset. In this paper,...

Using GMM-supervectors as the input to SVM classifiers (namely, GMM-SVM) is one of the promising approaches to text-independent speaker verification. However, one unaddressed issue of this approach is the severe imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM. This paper proposes a resampling technique – name...

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Other issues, such as within-class imbalance and high dimensionality, can exacerbate the problem. We propose a method HPSDRS that combines two ideas: Hybrid Probabilistic Sampling technique ensemble with Diverse Random Subspace to address these issues. HPS improves the performance of traditi...

Streaming data is pervasive in a multitude of data mining applications. One fundamental problem in the task of mining streaming data is distributional drift over time. Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. I...

Recent research has demonstrated the merit of combining Gaussian mixture models and support-vector-machine (SVM) for text-independent speaker verification. However, one unaddressed issue in this GMM–SVM approach is the imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM. This paper proposes a resampling technique...

The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors. Data imbalance is an inherent problem in activity recognition caused by the irregular time distribution of activities, which are sequential and dependent on previous movements. We use conditional random fields (CRF), a graphical model for stru...