University of Cincinnati
Computer Science
We consider a probability based approach according to which the similarity of two values (in the same domain) is the probability of value pairs whose components are rather apart than the two values under consideration. Similarities across... more
How to model a concept, and how to discover a new concept, remain fundamental in machine learning research. Real world concepts are usually high-dimensional and have complicated distributions. Gaussian Mixture Model has strength in... more
Content-based image retrieval has many applications but remains a computationally intensive task. This is mainly due to the large size of an image database required for practical use. Our project aims to examine existing CBIR... more
We propose a temporal regression model for dynamic graph link prediction problem, under spectral graph theory and low rank approximation for the graph Laplacian matrix. Link prediction is important in large-scale graphs including social... more
We propose a temporal regression model for dynamic graph link prediction problem, under spectral graph theory and low rank approximation for the graph Laplacian matrix. Link prediction is important in large-scale graphs including social... more
We present a novel framework in which the link prediction problem in temporal social networks is formulated as trajectory prediction in a continuous space. Four major modules constitute this framework: (1) graph embedding: the discrete... more
Curve Profiling Feature (CPF) is an innovative compact and discriminative feature for representing and mining the temporal-spatial patterns underlying Drosophila embryonic gene expressions from the Berkeley Drosophila Genome Project... more
P robSim-Annotation is an image annotation algorithm for heterogeneous data driven by a probability-based similarity assessment. Image annotation consists of associating to an image a description in terms of labels (or words) from a... more
Transcriptional regulatory network identification is both a fundamental challenge in systems biology and an important practical application of data mining and machine learning. In this study, we propose a semi-supervised learningbased... more
We discuss some relationships between probability theory and statistics on one hand, and the theory of fuzzy sets on the other hand. We develop various statistical techniques for the analysis of imprecise data and for inference based on... more
We discuss the equivalence between aggregation of fuzzy sets and integration with respect to a special class of nonadditive set functions. Both fuzzy integral and Choquet integral are considered. First, we study aggregation of a finite... more
We consider the problem of rule representation in expert systems when quantifiers are present. The approach is based on the concept of possibility distribution. Using this concept we are able to derive a formula for the possibility... more
We propose an alternative learning method for category classification knowledge. Our method induces a membership function for a category from positive and negative examples. It can learn "topological knowledge" such as typicality of an... more
This study evaluates the robustness of a fuzzy classifier when class distribution of the training set varies. The analysis of the results is based on the classification accuracy and ROC curves. The experimental results reported here show... more
This paper traces some of the recent progress in the field of learning of imbalanced data. It reviews approaches adopted for this problem and it identifies challenges and points out future directions in this relatively new field.
- by Anca Ralescu