H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 60 Citations 28,856 290 World Ranking 1567 National Ranking 32

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Operating system

Lior Rokach mainly focuses on Artificial intelligence, Data mining, Machine learning, Recommender system and Knowledge extraction. As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Subspace topology and Space decomposition. In general Data mining study, his work on Decision tree often relates to the realm of Decomposition, thereby connecting several areas of interest.

His research in Machine learning focuses on subjects like k-anonymity, which are connected to Data set, Information sensitivity and Data integrity. His Recommender system research incorporates elements of Context and Multimedia. His Knowledge extraction study frequently links to related topics such as Data science.

His most cited work include:

  • Recommender Systems Handbook (2000 citations)
  • Ensemble-based classifiers (1468 citations)
  • Introduction to Recommender Systems Handbook (1343 citations)

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

His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Recommender system and Decision tree. His Artificial intelligence research integrates issues from Natural language processing and Pattern recognition. The Machine learning study which covers Malware that intersects with Support vector machine.

Lior Rokach has researched Data mining in several fields, including Data science, Feature selection and Cluster analysis. His work focuses on many connections between Recommender system and other disciplines, such as Context, that overlap with his field of interest in Mobile device. His study in Decision tree concentrates on Decision tree learning and Alternating decision tree.

He most often published in these fields:

  • Artificial intelligence (46.48%)
  • Machine learning (34.73%)
  • Data mining (32.11%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (46.48%)
  • Machine learning (34.73%)
  • Deep learning (4.70%)

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

Lior Rokach mainly focuses on Artificial intelligence, Machine learning, Deep learning, Process and Malware. Lior Rokach interconnects Context and Pattern recognition in the investigation of issues within Artificial intelligence. His research related to Recommender system and Boosting might be considered part of Machine learning.

The various areas that Lior Rokach examines in his Recommender system study include Event and Database activity monitoring. The Artificial neural network study combines topics in areas such as Electronic engineering, Data mining and Distortion. His Data mining research is multidisciplinary, incorporating elements of Similarity and Encoding.

Between 2017 and 2021, his most popular works were:

  • Ensemble learning: A survey (268 citations)
  • Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers (51 citations)
  • CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments. (47 citations)

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

  • Artificial intelligence
  • Machine learning
  • Operating system

Lior Rokach mainly investigates Artificial intelligence, Machine learning, Malware, Classifier and Ensemble learning. His research brings together the fields of Pattern recognition and Artificial intelligence. His Machine learning study incorporates themes from Graph embedding, Mobile device and Measure.

His Malware research includes themes of Cloud computing and Support vector machine. Lior Rokach focuses mostly in the field of Classifier, narrowing it down to topics relating to Cluster analysis and, in certain cases, Mobile computing, Recommender system, Gradient boosting and Feature engineering. His Random forest study integrates concerns from other disciplines, such as Decision tree and Boosting.

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

Introduction to Recommender Systems Handbook

Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2011)

5976 Citations

Recommender Systems Handbook

Francesco Ricci;Lior Rokach;Bracha Shapira;Paul B. Kantor.
rsh (2010)

3437 Citations

Data Mining and Knowledge Discovery Handbook

Oded Maimon;Lior Rokach.
(2005)

2422 Citations

Data Mining with Decision Trees: Theory and Applications

Lior Rokach;Oded Maimon.
(2007)

2317 Citations

Ensemble-based classifiers

Lior Rokach.
Artificial Intelligence Review (2010)

2259 Citations

Recommender Systems: Introduction and Challenges

Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2015)

1681 Citations

Top-down induction of decision trees classifiers - a survey

L. Rokach;O. Maimon.
systems man and cybernetics (2005)

857 Citations

Data Mining and Knowledge Discovery Handbook, 2nd ed

Oded Z. Maimon;Lior Rokach.
(2010)

393 Citations

Ensemble learning: A survey

Omer Sagi;Lior Rokach.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (2018)

389 Citations

Pattern Classification Using Ensemble Methods

Lior Rokach.
(2009)

363 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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