Most classification algorithms expect the frequency of examples form each class to be roughly the same. However, this is rarely the case for real-world data where very often the class probability distribution is nonuniform (or, imbalanced). For these applications, the main problem is usually the fact that the costs of misclassifying examples belonging to rare classes differ significantly from t...

This paper experimentally compares the performance of discriminative and generative classifiers for cost sensitive learning. There is some evidence that learning a discriminative classifier is more effective for a traditional classification task. This paper explores the advantages, and disadvantages, of using a generative classifier when the misclassification costs, and class frequencies, are n...

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, exist...

Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we investigate the problem of online active learning within a new active domain adaptation setting: there are insufficient labeled data in both source and target domains, but it is cheaper to query labels in the source domain than in the...

Recommendation is the task of improving customer experience through personalized recommendation based on users’ past feedback. In this paper, we investigate the most common scenario: the user-item (U-I) matrix of implicit feedback (e.g. clicks, views, purchases). Even though many recommendation approaches are designed based on implicit feedback, they attempt to project the U-I matrix into a low...

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an u...

A lot of practical machine learning applications deal with interactive classification problems where trained classifiers are used to help humans find positive examples that are of interest to them. Typically, these classifiers label a large number of test examples and present the humans with a ranked list to review. The humans involved in this process are often expensive domain experts with lim...

We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the costsensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic oneversus-one decomposition. While such a näıve reduction results in exponentiallymany classi...

Assigning functional classes to unknown genes or proteins on diverse large-scale data is a key task in biological systems, and it needs the integration of different data sources and the analysis of functional hierarchies. In this paper we present a method based on Hopfield neural networks which is a variant of a precedent semi-supervised approach that transfers protein functions from annotated ...