H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 66 Citations 48,821 258 World Ranking 1076 National Ranking 633

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

His most cited work include:

  • SMOTE: synthetic minority over-sampling technique (9316 citations)
  • SMOTE: Synthetic Minority Over-sampling Technique (4150 citations)
  • Editorial: special issue on learning from imbalanced data sets (1403 citations)

What are the main themes of his work throughout his whole career to date?

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

  • Theoretical computer science, which have a strong connection to Embedding,
  • Network science that intertwine with fields like Dynamic network analysis. His Data science research includes themes of Health informatics, Knowledge extraction and Big data. His work deals with themes such as Disease and Personalized medicine, which intersect with Health care.

He most often published in these fields:

  • Artificial intelligence (35.49%)
  • Machine learning (29.63%)
  • Data mining (27.47%)

What were the highlights of his more recent work (between 2018-2020)?

  • Artificial intelligence (35.49%)
  • Embedding (3.70%)
  • Machine learning (29.63%)

In recent papers he was focusing on the following fields of study:

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.

Between 2018 and 2020, his most popular works were:

  • Heterogeneous Graph Neural Network (163 citations)
  • A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (81 citations)
  • SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks (31 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

SMOTE: Synthetic Minority Over-sampling Technique

N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer.
arXiv: Artificial Intelligence (2011)

13046 Citations

SMOTE: synthetic minority over-sampling technique

Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer.
Journal of Artificial Intelligence Research (2002)

11696 Citations

SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS

N Chawla;N Japkowicz;A Kolcz.
(2004)

2276 Citations

Editorial: special issue on learning from imbalanced data sets

Nitesh V. Chawla;Nathalie Japkowicz;Aleksander Kotcz.
Sigkdd Explorations (2004)

2241 Citations

Data Mining for Imbalanced Datasets: An Overview

Nitesh V. Chawla.
The Data Mining and Knowledge Discovery Handbook (2005)

1447 Citations

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)

1326 Citations

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami.
knowledge discovery and data mining (2017)

909 Citations

SVMs Modeling for Highly Imbalanced Classification

Yuchun Tang;Yan-Qing Zhang;N.V. Chawla;S. Krasser.
systems man and cybernetics (2009)

872 Citations

New perspectives and methods in link prediction

Ryan N. Lichtenwalter;Jake T. Lussier;Nitesh V. Chawla.
knowledge discovery and data mining (2010)

759 Citations

A unifying view on dataset shift in classification

Jose G. Moreno-Torres;Troy Raeder;RocíO Alaiz-RodríGuez;Nitesh V. Chawla.
Pattern Recognition (2012)

440 Citations

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