457 citations · 725 across the 16 of their papers we have counts for
7 papers · 1 filter
Leveraging Structure for Improved Classification of Grouped Biased Data
Daniel Zeiberg, Shantanu Jain, Predrag Radivojac
We consider semi-supervised binary classification for applications in which data points are naturally grouped (e.g., survey responses grouped by state) and the labeled data is bias…
Classification in biological networks with hypergraphlet kernels
Jose Lugo-Martinez, Predrag Radivojac
Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these…
Recovering True Classifier Performance in Positive-Unlabeled Learning
Shantanu Jain, Martha White, Predrag Radivojac
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier…
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
Estimating the class prior and posterior from noisy positives and unlabeled data
Shantanu Jain, Martha White, Predrag Radivojac
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dim…
A new class of metrics for learning on real-valued and structured data
Ruiyu Yang, Yuxiang Jiang, Scott Mathews +3
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result inter…
Nonparametric semi-supervised learning of class proportions
Shantanu Jain, Martha White, Michael W. Trosset +1
The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate poste…