Nitesh V. Chawla mainly focuses on Artificial intelligence, Machine learning, Data mining, Big data and Sampling. His research ties Pattern recognition and Artificial intelligence together. His biological study spans a wide range of topics, including Social network and Complex network.
His Intrusion detection system study in the realm of Data mining interacts with subjects such as Link. His Big data research is multidisciplinary, incorporating elements of Reimbursement, Patient-centered outcomes and Data science. His Classifier study incorporates themes from Prior probability, Naive Bayes classifier and Receiver operating characteristic.
Artificial intelligence, Machine learning, Data mining, Data science and Health care are his primary areas of study. His Pattern recognition research extends to Artificial intelligence, which is thematically connected. His Machine learning research integrates issues from Sampling and Complex network.
His Data mining study also includes
Nitesh V. Chawla focuses on Artificial intelligence, Embedding, Machine learning, Theoretical computer science and Analytics. The study incorporates disciplines such as Multivariate statistics and Pattern recognition in addition to Artificial intelligence. His Pattern recognition research incorporates themes from Silhouette, Time series, Data set and Isosurface.
His Embedding study incorporates themes from Temporal database, Activity recognition, Feature learning and Time series classification. His Machine learning research incorporates elements of Event forecasting and Masking. His Theoretical computer science research is multidisciplinary, incorporating elements of Node, Graph neural networks, Graph and Cluster analysis.
His main research concerns Artificial intelligence, Machine learning, Artificial neural network, Embedding and Theoretical computer science. His Artificial intelligence study combines topics from a wide range of disciplines, such as Multivariate statistics and Relevance. He performs integrative study on Machine learning and Mist.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Feature, Time series, Recommender system, Anomaly and Pattern recognition. The concepts of his Embedding study are interwoven with issues in Graph neural networks, Graph and Feature learning. His studies deal with areas such as Leverage and Cluster analysis as well as Graph.
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SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer.
arXiv: Artificial Intelligence (2011)
SMOTE: synthetic minority over-sampling technique
Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer.
Journal of Artificial Intelligence Research (2002)
SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS
N Chawla;N Japkowicz;A Kolcz.
Editorial: special issue on learning from imbalanced data sets
Nitesh V. Chawla;Nathalie Japkowicz;Aleksander Kotcz.
Sigkdd Explorations (2004)
Data Mining for Imbalanced Datasets: An Overview
Nitesh V. Chawla.
The Data Mining and Knowledge Discovery Handbook (2005)
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer.
european conference on principles of data mining and knowledge discovery (2003)
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami.
knowledge discovery and data mining (2017)
SVMs Modeling for Highly Imbalanced Classification
Yuchun Tang;Yan-Qing Zhang;N.V. Chawla;S. Krasser.
systems man and cybernetics (2009)
New perspectives and methods in link prediction
Ryan N. Lichtenwalter;Jake T. Lussier;Nitesh V. Chawla.
knowledge discovery and data mining (2010)
A unifying view on dataset shift in classification
Jose G. Moreno-Torres;Troy Raeder;RocíO Alaiz-RodríGuez;Nitesh V. Chawla.
Pattern Recognition (2012)
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