D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 58 Citations 23,586 151 World Ranking 1747 National Ranking 967

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Machine learning, Artificial intelligence, Data mining, Classifier and Training set. He has included themes like Quality, Pattern recognition and Word error rate in his Machine learning study. Foster Provost works mostly in the field of Artificial intelligence, limiting it down to topics relating to Focus and, in certain cases, Contrast, as a part of the same area of interest.

His Data mining research includes themes of Automatic summarization, Imbalanced data and Consumer behaviour. His Classifier study incorporates themes from Lift, Decision analysis and Statistical relational learning. He interconnects Expected utility hypothesis, Sampling, Area under the roc curve and Gibbs sampling in the investigation of issues within Training set.

His most cited work include:

  • Robust Classification for Imprecise Environments (1032 citations)
  • The Case against Accuracy Estimation for Comparing Induction Algorithms (958 citations)
  • Get another label? improving data quality and data mining using multiple, noisy labelers (800 citations)

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

Foster Provost mainly investigates Artificial intelligence, Machine learning, Data mining, Data science and Classifier. The concepts of his Artificial intelligence study are interwoven with issues in Statistical relational learning and Pattern recognition. The various areas that Foster Provost examines in his Machine learning study include Training set and Expected utility hypothesis.

Foster Provost focuses mostly in the field of Data mining, narrowing it down to topics relating to Quality and, in certain cases, Crowdsourcing. The study incorporates disciplines such as Data-driven, Knowledge extraction, Set and Big data in addition to Data science. His research on Classifier frequently links to adjacent areas such as Decision analysis.

He most often published in these fields:

  • Artificial intelligence (41.63%)
  • Machine learning (38.20%)
  • Data mining (26.61%)

What were the highlights of his more recent work (between 2013-2021)?

  • Artificial intelligence (41.63%)
  • Machine learning (38.20%)
  • Data mining (26.61%)

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

His main research concerns Artificial intelligence, Machine learning, Data mining, Big data and Data science. His Artificial intelligence research is mostly focused on the topic Learning models. Foster Provost undertakes interdisciplinary study in the fields of Machine learning and Outcome through his research.

His work on Classifier expands to the thematically related Data mining. His Big data study combines topics from a wide range of disciplines, such as Need to know, Text mining, Data analysis, Payment and Process. His Data science research incorporates elements of Intelligent decision support system, Data-driven and Control.

Between 2013 and 2021, his most popular works were:

  • Repeated labeling using multiple noisy labelers (126 citations)
  • Explaining data-driven document classifications (120 citations)
  • Machine learning for targeted display advertising: transfer learning in action (100 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Data-driven, Data science, Machine learning, Artificial intelligence and Display advertising are his primary areas of study. His research investigates the connection between Data-driven and topics such as Set that intersect with issues in Econometrics, Feature and Contrast. His work carried out in the field of Data science brings together such families of science as Class, Document classification, Web page and Big data.

Foster Provost is involved in the study of Machine learning that focuses on Active learning in particular. Proxy and Lift is closely connected to Identification in his research, which is encompassed under the umbrella topic of Display advertising. His research in Advertising campaign focuses on subjects like Data mining, which are connected to Empirical research.

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

Robust Classification for Imprecise Environments

Foster Provost;Tom Fawcett.
Machine Learning (2001)

1569 Citations

The Case against Accuracy Estimation for Comparing Induction Algorithms

Foster J. Provost;Tom Fawcett;Ron Kohavi.
international conference on machine learning (1998)

1257 Citations

Adaptive Fraud Detection

Tom Fawcett;Foster Provost.
Data Mining and Knowledge Discovery (1997)

1256 Citations

DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING

Foster J. Provost;Tom Fawcett.
Big data (2013)

1203 Citations

Get another label? improving data quality and data mining using multiple, noisy labelers

Victor S. Sheng;Foster Provost;Panagiotis G. Ipeirotis.
knowledge discovery and data mining (2008)

1189 Citations

Quality management on Amazon Mechanical Turk

Panagiotis G. Ipeirotis;Foster Provost;Jing Wang.
knowledge discovery and data mining (2010)

1160 Citations

Learning when training data are costly: the effect of class distribution on tree induction

Gary M. Weiss;Foster Provost.
Journal of Artificial Intelligence Research (2003)

1073 Citations

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Foster Provost;Tom Fawcett.
(2013)

996 Citations

Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions

Foster Provost;Tom Fawcett.
knowledge discovery and data mining (1997)

840 Citations

Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

Shawndra Hill;Foster Provost;Chris Volinsky.
Statistical Science (2006)

758 Citations

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