H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 34 Citations 10,660 138 World Ranking 6344 National Ranking 3096

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Incremental learning and Classifier. The various areas that he examines in his Artificial intelligence study include Pattern recognition and Concept drift. His Concept drift research is multidisciplinary, relying on both Algorithm design and Problem domain.

The Machine learning study combines topics in areas such as Decision support system, Data mining and Data set. His work deals with themes such as Unsupervised learning and Computer network, which intersect with Artificial neural network. The Incremental learning study which covers Training set that intersects with Support vector machine.

His most cited work include:

  • Ensemble based systems in decision making (1942 citations)
  • Learn++: an incremental learning algorithm for supervised neural networks (648 citations)
  • Incremental Learning of Concept Drift in Nonstationary Environments (534 citations)

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

His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Incremental learning. His studies deal with areas such as Data mining and Concept drift as well as Artificial intelligence. The various areas that he examines in his Concept drift study include Algorithm design, Labeled data and Computational intelligence.

His research integrates issues of Disease and Electroencephalography in his study of Machine learning. Robi Polikar works in the field of Artificial neural network, focusing on Multilayer perceptron in particular. His Incremental learning research includes themes of Semi-supervised learning, AdaBoost and Benchmark.

He most often published in these fields:

  • Artificial intelligence (58.76%)
  • Machine learning (41.24%)
  • Pattern recognition (21.65%)

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

  • Artificial intelligence (58.76%)
  • Machine learning (41.24%)
  • Concept drift (12.89%)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Concept drift, Data mining and Feature selection. In his study, Recursive Bayesian estimation is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence. His Machine learning research integrates issues from Classifier, Adversary and Task.

Robi Polikar has researched Concept drift in several fields, including Statistical classification, Labeled data and Computational intelligence. His Data mining research is multidisciplinary, incorporating elements of Competitive learning, Deep learning, Unsupervised learning and Feature learning. His biological study spans a wide range of topics, including Statistical hypothesis testing, Cross-validation and Dimensionality reduction.

Between 2013 and 2021, his most popular works were:

  • Learning in Nonstationary Environments: A Survey (348 citations)
  • IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (130 citations)
  • COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data (96 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Machine learning, Feature extraction, Data mining and Data set. He is interested in Feature selection, which is a branch of Artificial intelligence. His work carried out in the field of Data mining brings together such families of science as Competitive learning, Recurrent neural network, Types of artificial neural networks and Deep belief network.

His Data set research incorporates themes from Probability distribution and Algorithm design. He combines subjects such as Labeled data, Computational intelligence and Cluster analysis with his study of Concept drift. Many of his research projects under Artificial neural network are closely connected to IEEE 802 with IEEE 802, tying the diverse disciplines of science together.

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.

Top Publications

Ensemble based systems in decision making

R. Polikar.
IEEE Circuits and Systems Magazine (2006)

2778 Citations

Learn++: an incremental learning algorithm for supervised neural networks

R. Polikar;L. Upda;S.S. Upda;V. Honavar.
systems man and cybernetics (2001)

965 Citations

Incremental Learning of Concept Drift in Nonstationary Environments

R. Elwell;R. Polikar.
IEEE Transactions on Neural Networks (2011)

740 Citations

Learning in Nonstationary Environments: A Survey

Gregory Ditzler;Manuel Roveri;Cesare Alippi;Robi Polikar.
IEEE Computational Intelligence Magazine (2015)

478 Citations

Incremental Learning of Concept Drift from Streaming Imbalanced Data

Gregory Ditzler;Robi Polikar.
IEEE Transactions on Knowledge and Data Engineering (2013)

299 Citations

Learn $^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes

M.D. Muhlbaier;A. Topalis;R. Polikar.
IEEE Transactions on Neural Networks (2009)

258 Citations

Learning from streaming data with concept drift and imbalance: an overview

T. Ryan Hoens;Robi Polikar;Nitesh V. Chawla.
Progress in Artificial Intelligence (2012)

241 Citations

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Derong Liu;Murad Abu-Khalaf;Adel M. Alimi;Charles Anderson.
(2015)

196 Citations

Bootstrap - Inspired Techniques in Computation Intelligence

R. Polikar.
IEEE Signal Processing Magazine (2007)

173 Citations

An Ensemble-Based Incremental Learning Approach to Data Fusion

D. Parikh;R. Polikar.
systems man and cybernetics (2007)

140 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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