Supervised and semi-supervised data mining techniques require labeled data. However, labeling examples is costly for many real-world applications. To address this problem, active learning techniques have been developed to guide the labeling process in an effort to minimize the amount of labeled data without sacrificing much from the quality of the learned models. Yet, most of the active learnin...

In an effort to identify genes related to the cell line chemosensitivity and to evaluate the functional relationships between genes and anticancer drugs acting by the same mechanism, a supervised machine learning approach called support vector machine was used to label genes into any of the five predefined anticancer drug mechanistic categories. Among dozens of unequivocally categorized genes, ...

In multi-class categorization tasks, knowledge about the classes’ semantic relationships can provide valuable information beyond the class labels themselves. However, existing techniques focus on preserving the semantic distances between classes (e.g., according to a given object taxonomy for visual recognition), limiting the influence to pairwise structures. We propose to model analogies that ...

Link prediction is an important task in Social Network Analysis. This problem refers to predicting the emergence of future relationships between nodes in a social network. Our work focuses on a supervised machine learning approach for link prediction. Here, the target attribute is a class label indicating the existence or absence of a link between a node pair. The predictor attributes are metri...

• The local imbalance is more crucial than the global one in multi-label data. based measure assesses hardness of MLSOL and MLUL tackle class issue via imbalance. Suitable application situations our two methods are identified, respectively. Class an inherent characteristic data that hinders most learning methods. One efficient flexible strategy to deal with this problem employ sampling techniqu...