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D-Index & Metrics

Engineering and Technology

D-Index
38
Citations
11389
World Ranking
7885
National Ranking
318

Research.com Recognitions

  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering

Overview

Charles X. Ling is affiliated with the University of Western Ontario in Canada. Their research primarily focuses on the field of Computer Science, with particular emphasis on Artificial Intelligence, Computer Vision and Pattern Recognition, and related subfields. They have a significant number of publications that contribute to various areas of machine learning and neural networks.

The main topics covered in their work include:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Topic Modeling
  • Privacy-Preserving Technologies in Data
  • Advanced Neural Network Applications

Among their recent papers are the following:

  • "When Source-Free Domain Adaptation Meets Learning with Noisy Labels" (2023), arXiv (Cornell University)
  • "Ensemble Learning With Attention-Integrated Convolutional Recurrent Neural Network for Imbalanced Speech Emotion Recognition" (2020), IEEE Access
  • "On Learning Fairness and Accuracy on Multiple Subgroups" (2022), arXiv (Cornell University)
  • "Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation" (2023), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Episodic task agnostic contrastive training for multi-task learning" (2023), Neural Networks

Frequent co-authors who have collaborated extensively with Charles X. Ling include:

  • Boyu Wang
  • Ruizhi Pu
  • Changjian Shui
  • Gezheng Xu

The venues where the researcher has frequently published are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neural Networks
  • IEEE Transactions on Knowledge and Data Engineering
  • Scientific Reports

Charles X. Ling has also been recognized by The Canadian Academy of Engineering.

Best Publications

  • Using AUC and accuracy in evaluating learning algorithms

    Jin Huang;C.X. Ling

  • Data mining for direct marketing: problems and solutions

    Charles X. Ling;Chenghui Li

  • AUC: a statistically consistent and more discriminating measure than accuracy

    Charles X. Ling;Jin Huang;Harry Zhang

  • AUC: a better measure than accuracy in comparing learning algorithms

    Charles X. Ling;Jin Huang;Harry Zhang

  • DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

    Wenyin Gong;Zhihua Cai;Charles X. Ling

  • Decision trees with minimal costs

    Charles X. Ling;Qiang Yang;Jianning Wang;Shichao Zhang

  • Pelee: a real-time object detection system on mobile devices

    Robert J. Wang;Xiang Li;Charles X. Ling

  • Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

    J. Huang;J. Lu;C.X. Ling

  • Cost-Sensitive Learning and the Class Imbalance Problem

    Charles X. Ling;Victor S. Sheng

  • Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization

    Wenyin Gong;Zhihua Cai;Charles X Ling;Hui Li

  • "Missing is useful": missing values in cost-sensitive decision trees

    Shichao Zhang;Z. Qin;C.X. Ling;S. Sheng

  • Answering the connectionist challenge: a symbolic model of learning the past tenses of English verbs

    Charles X. Ling;Marin Marinov

  • Test-cost sensitive naive Bayes classification

    Xiaoyong Chai;Lin Deng;Qiang Yang;C.X. Ling

  • Thresholding for making classifiers cost-sensitive

    Victor S. Sheng;Charles X. Ling

  • Test strategies for cost-sensitive decision trees

    C.X. Ling;V.S. Sheng;Q. Yang

  • A real-coded biogeography-based optimization with mutation

    Wenyin Gong;Zhihua Cai;Charles X. Ling;Hui Li

  • pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

    Dequan Li;Yan Fu;Ruixiang Sun;Charles X. Ling

  • Discriminative parameter learning for Bayesian networks

    Jiang Su;Harry Zhang;Charles X. Ling;Stan Matwin

  • A clustering-based differential evolution for global optimization

    Zhihua Cai;Wenyin Gong;Charles X. Ling;Harry Zhang

  • Reviewer Recommender of Pull-Requests in GitHub

    Yue Yu;Huaimin Wang;Gang Yin;Charles X. Ling

  • Cost-Sensitive Learning.

    Charles X. Ling;Victor S. Sheng

  • Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry

    Yan Fu;Qiang Yang;Ruixiang Sun;Dequan Li

  • Extracting Actionable Knowledge from Decision Trees

    Qiang Yang;Jie Yin;C. Ling;Rong Pan

  • Keyphrase Extraction Using Semantic Networks Structure Analysis

    Chong Huang;Yonghong Tian;Zhi Zhou;C.X. Ling

  • Postprocessing decision trees to extract actionable knowledge

    Qiang Yang;Jie Yin;C.X. Ling;T. Chen

  • Test-cost sensitive classification on data with missing values

    Qiang Yang;C. Ling;X. Chai;Rong Pan

  • Simple test strategies for cost-sensitive decision trees

    Shengli Sheng;Charles X. Ling;Qiang Yang

  • Machine learning for stock selection

    Robert J. Yan;Charles X. Ling

Frequent Co-Authors

Victor S. Sheng
Victor S. Sheng Texas Tech University
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Wen Gao
Wen Gao Peking University
Stan Matwin
Stan Matwin Dalhousie University
Wenyin Gong
Wenyin Gong China University of Geosciences
Yiqiang Chen
Yiqiang Chen Chinese Academy of Sciences
Wai Lam
Wai Lam Chinese University of Hong Kong
Jinhua Zheng
Jinhua Zheng Xiangtan University
David W. Aha
David W. Aha United States Naval Research Laboratory
Rong Zeng
Rong Zeng Chinese Academy of Sciences

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