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Nitesh V. Chawla
Researcher at University of Notre Dame
Publications - 434
Citations - 52969
Nitesh V. Chawla is an academic researcher from University of Notre Dame. The author has contributed to research in topics: Computer science & Health care. The author has an hindex of 61, co-authored 388 publications receiving 41365 citations. Previous affiliations of Nitesh V. Chawla include University of South Florida & Wrocław University of Technology.
Papers
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Proceedings ArticleDOI
SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks
TL;DR: A Semantic-aware Heterogeneous Network Embedding model (SHNE) is developed that performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network.
Proceedings ArticleDOI
Decision tree learning on very large data sets
TL;DR: Results from cross validation experiments on a data set suggest this approach to generating the rules for an agent from a large training set may be effectively applied to large sets of data.
Proceedings ArticleDOI
MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting
TL;DR: A Multi-View and Multi-Modal Spatial-Temporal learning (MiST) framework to address the above challenges by promoting the collaboration of different views (spatial, temporal and semantic) and map the multi-modal units into the same latent space.
Proceedings ArticleDOI
Link Prediction: Fair and Effective Evaluation
R. Lichtnwalter,Nitesh V. Chawla +1 more
TL;DR: It is argued for the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to the extreme imbalance of the link prediction classification problem.
Proceedings ArticleDOI
MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable
TL;DR: A layer which longitudinally interprets both predictions and entire classification models of MOOC dropout to provide researchers with in-depth insights of why a student is likely to dropout is demonstrated.