D-Index & Metrics Best Publications
Computer Science
New Zealand
2022

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 55 Citations 15,058 611 World Ranking 2195 National Ranking 4

Research.com Recognitions

Awards & Achievements

2022 - Research.com Computer Science in New Zealand Leader Award

2019 - IEEE Fellow For contributions to evolutionary learning and optimization methodologies

2017 - Fellow of the Royal Society of New Zealand

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His main research concerns Artificial intelligence, Pattern recognition, Genetic programming, Machine learning and Feature selection. His studies deal with areas such as Genetic algorithm, Fitness function and Particle swarm optimization as well as Artificial intelligence. His biological study spans a wide range of topics, including Cognitive neuroscience of visual object recognition, Word error rate, Decision tree, Contextual image classification and Entropy.

His Genetic programming study combines topics in areas such as Object, Object detection, Class, Algorithm and Job shop scheduling. His Feature selection research is multidisciplinary, relying on both Data mining, Feature, Curse of dimensionality, Mutual information and Evolutionary computation. His studies in Feature extraction integrate themes in fields like Symbolic regression and Linear classifier.

His most cited work include:

  • A Survey on Evolutionary Computation Approaches to Feature Selection (659 citations)
  • Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (602 citations)
  • Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation (364 citations)

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

His primary areas of study are Artificial intelligence, Genetic programming, Machine learning, Pattern recognition and Feature selection. His Artificial intelligence study deals with Particle swarm optimization intersecting with Benchmark. His study in Genetic programming is interdisciplinary in nature, drawing from both Genetic algorithm, Mathematical optimization, Fitness function and Job shop scheduling.

His Machine learning research integrates issues from Classifier, Training set and Set. His Pattern recognition research includes elements of Decision tree, Object detection and Computer vision. The Feature selection study combines topics in areas such as Data mining and Curse of dimensionality.

He most often published in these fields:

  • Artificial intelligence (66.89%)
  • Genetic programming (55.05%)
  • Machine learning (37.63%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (66.89%)
  • Genetic programming (55.05%)
  • Machine learning (37.63%)

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

Mengjie Zhang mainly investigates Artificial intelligence, Genetic programming, Machine learning, Pattern recognition and Feature extraction. His Artificial intelligence and Contextual image classification, Convolutional neural network, Feature selection, Evolutionary computation and Feature learning investigations all form part of his Artificial intelligence research activities. The concepts of his Feature selection study are interwoven with issues in Feature, Curse of dimensionality, Particle swarm optimization, Cluster analysis and Evolutionary algorithm.

His research in Genetic programming intersects with topics in Transfer of learning, Routing, Mathematical optimization and Job shop scheduling. His Machine learning research incorporates themes from Tree, Multi-task learning, Training set and Domain. In his work, Binary number is strongly intertwined with Domain knowledge, which is a subfield of Pattern recognition.

Between 2019 and 2021, his most popular works were:

  • Evolving Deep Convolutional Neural Networks for Image Classification (111 citations)
  • Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (99 citations)
  • Completely Automated CNN Architecture Design Based on Blocks (41 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

His primary areas of investigation include Artificial intelligence, Genetic programming, Machine learning, Feature extraction and Feature selection. His Artificial intelligence study combines topics from a wide range of disciplines, such as Set and Pattern recognition. His work in the fields of Genetic programming, such as Symbolic regression, intersects with other areas such as Hyper-heuristic.

His work on Transfer of learning as part of his general Machine learning study is frequently connected to Term, thereby bridging the divide between different branches of science. Mengjie Zhang combines subjects such as Cluster analysis, Selection, Feature and Curse of dimensionality with his study of Feature selection. His study looks at the intersection of Evolutionary computation and topics like Field with 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

Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

Bing Xue;Mengjie Zhang;Will N. Browne.
IEEE Transactions on Systems, Man, and Cybernetics (2013)

819 Citations

A Survey on Evolutionary Computation Approaches to Feature Selection

Bing Xue;Mengjie Zhang;Will N. Browne;Xin Yao.
IEEE Transactions on Evolutionary Computation (2016)

666 Citations

Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang;David Balduzzi.
european conference on computer vision (2016)

430 Citations

Particle swarm optimisation for feature selection in classification

Bing Xue;Mengjie Zhang;Will N. Browne.
soft computing (2014)

422 Citations

Domain Generalization for Object Recognition with Multi-task Autoencoders

Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang;David Balduzzi.
international conference on computer vision (2015)

266 Citations

Automated Design of Production Scheduling Heuristics: A Review

Jurgen Branke;Su Nguyen;Christoph W. Pickardt;Mengjie Zhang.
IEEE Transactions on Evolutionary Computation (2016)

224 Citations

Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction

Rohitash Chandra;Mengjie Zhang.
Neurocomputing (2012)

204 Citations

Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

U. Bhowan;M. Johnston;Mengjie Zhang;Xin Yao.
IEEE Transactions on Evolutionary Computation (2013)

203 Citations

Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

Muhammad Ghifary;David Balduzzi;W. Bastiaan Kleijn;Mengjie Zhang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

194 Citations

Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

Su Nguyen;Mengjie Zhang;Mark Johnston;Kay Chen Tan.
IEEE Transactions on Evolutionary Computation (2014)

178 Citations

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