Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, ...

This paper introduces a learning system iBARET (Instance-BAsed REasoning Tool). This system takes a traditional instance-based learning approach and enhances it from several points of view. It enables to solve regression as well as classification tasks. Several techniques of feature weighting combined with different methods of the model evaluation are implemented. In order to provide an effecti...

We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current label as a weight during training. The probability vector should be calculated such that clean instances have a high confidence on its current label, while mislabeled instances have a low confidence on its current labe...

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clus...

of the Dissertation Spectral Clustering for Complex Settings Many real-world datasets can be modeled as graphs, where each node corresponds to a data instance and an edge represents the relation/similarity between two nodes. To partition the nodes into different clusters, spectral clustering is used to find the normalized minimum cut of the graph (in the relaxed sense). As one of the most popul...

Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classification systems struggle to achieve good performance on less common relation types, which capture valuable information that is important to identify. O...

Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the clustering per...