Research output

Research output per year 2006 2017 2018 2019 2020 2021 2022 2023 2024 2024

• 97 Article
• 55 Conference Proceeding (Non-Journal item)
• 15 Paper
• 1 Other chapter contribution
• 1 More
  • 1 Working paper

Research output per year

Research output per year

Search results

• 2013

  Fuzzy-Rough Feature Selection Aided Support Vector Machines for Mars Image Classification

  Shang, C. & Barnes, D., Mar 2013, In: Computer Vision and Image Understanding. 117, 3, p. 202–213 12 p.

  Research output: Contribution to journal › Article › peer-review

  • Feature Selection 100%
  • Classification (Machine Learning) 83%
  • Image Classification 50%
  • Support Vector Machine 50%
  • Nearest Neighbour 33%
  49 Citations (Scopus)

• Fuzzy similarity-based nearest-neighbour classification as alternatives to their fuzzy-rough parallels

  Qu, Y., Shen, Q., MacParthaláin, N. S., Shang, C. & Wu, W., Jan 2013, In: International Journal of Approximate Reasoning. 54, 1, p. 184-195 12 p.

  Research output: Contribution to journal › Article › peer-review

  • Fuzzy 100%
  • Nearest Neighbour 72%
  • Rough Fuzzy Set 27%
  • Evaluation 27%
  • Classification 18%
  24 Citations (Scopus)

• Interpolating DeSTIN features for image classification

  Zhang, Y., Shang, C. & Shen, Q., 2013, Proceedings of the 2013 UK Workshop on Computational Intelligence. IEEE Press, p. 292-298 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  1 Citation (Scopus)

• Link-based Approach for Bibliometric Journal Ranking

  Su, P., Shang, C. & Shen, Q., 2013, In: Soft Computing. 17, 12, p. 2399-2410

  Research output: Contribution to journal › Article › peer-review

  • Links 100%
  • Indicator 80%
  • Accuracy 40%
  • Procedure 40%
  • Supervised Learning 20%
  13 Citations (Scopus)

• Mars Multipspectral Image Classification Using Machine Learning Techniques

  Pan, L., Gui, C., Barnes, D. & Shang, C., 2013.

  Research output: Contribution to conference › Paper › peer-review

  • Image Classification 100%
  • Rock 100%
  • Multispectral Data 66%
  • Detection 66%
  • Machine Learning Technique 66%

• Modified gradient based learning for local coupled feedforward neural networks with Gaussian basis function

  Qu, Y., Shang, C., Yang, J., Wu, W. & Shen, Q., 01 May 2013, In: Neural Computing and Applications. 22, 1 (Supplement), p. 379-394

  Research output: Contribution to journal › Article › peer-review
  1 Citation (Scopus)

• OWA-aggregated fuzzy similarity relations for journal ranking

  Su, P., Shang, C. & Shen, Q., 2013, Proceedings of the 22nd International Conference on Fuzzy Systems. IEEE Press, p. 1-7 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  • Weighted Average 100%
  • Accuracy 66%
  • Vectors 66%
  • Ranking 66%
  • Impact Journal 33%
  7 Citations (Scopus)
• 2012

  Support vector machine-based classification of rock texture images aided by efficient feature selection

  Shang, C. & Barnes, D., 2012, The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE Press, p. 1-8 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  • Image Classification 100%
  • Classification 37%
  • Selected Feature 37%
  • Feature Selection 37%
  • Feature Extraction 37%
  17 Citations (Scopus)
• 2011

  Classification of Mars McMurdo panorama images using machine learning techniques

  Shang, C. & Barnes, D. P., 25 Oct 2011.

  Research output: Contribution to conference › Paper

  File

  • Feature Selection 100%
  • Classification (Machine Learning) 75%
  • Machine Learning Technique 50%
  • Support Vector Machine 50%
  • Experimental Result 25%
  42 Downloads (Pure)

• Evolutionary Fuzzy Extreme Learning Machine for Mammographic Risk Analysis

  Qu, Y., Shang, C., Wu, W. & Shen, Q., Dec 2011, In: International Journal of Fuzzy Systems. 13, 4, p. 282-291 10 p., 2552658.

  Research output: Contribution to journal › Article › peer-review

  • Extreme Learning Machine 100%
  • Classification (Machine Learning) 33%
  • Confusion Matrix 33%
  • Automated Analysis 33%
  • Classification Accuracy 33%
  55 Citations (Scopus)

• Extending data reliability measure to a filter approach for soft subspace clustering

  Boongoen, T., Shang, C., Iam-On, N. & Shen, Q., 12 Dec 2011, In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 41, 6, p. 1705-1714 10 p., 5962370.

  Research output: Contribution to journal › Article › peer-review

  File

  • Data Reliability 100%
  • Clustering 100%
  • Data Attribute 50%
  • Evaluation 50%
  • Clustering Method 50%
  52 Citations (Scopus)

  242 Downloads (Pure)

• Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection

  Barnes, D. P., Shang, C. & Shen, Q., 2011, In: International Journal of Hybrid Intelligent Systems. 8, 1, p. 3-13 11 p.

  Research output: Contribution to journal › Article › peer-review

  File

  • Image Classification 100%
  • Feature Selection 44%
  • Feature Extraction 44%
  • Mars 33%
  • Nearest Neighbour 11%
  233 Downloads (Pure)

• Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

  Qu, Y., Shang, C., Shen, Q., MacParthaláin, N. S. & Wu, W., 06 Sept 2011, Proceedings of the 20th IEEE International Conference on Fuzzy Systems. IEEE Press, p. 1523-1529 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  File

  • Rough Fuzzy Set 100%
  • Nearest Neighbour 75%
  • Kernel Method 50%
  • Roles 25%
  • Evaluation 25%
  20 Citations (Scopus)

  232 Downloads (Pure)
• 2010

  Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images

  Barnes, D. P. & Shang, C., 2010, p. 1061-1064. 4 p.

  Research output: Contribution to conference › Paper

  File

  • Classification (Machine Learning) 100%
  • Support Vector Machine 75%
  • Information Gain 75%
  • Image Classification 25%
  • Application 25%
  9 Citations (Scopus)

  176 Downloads (Pure)
• 2009

  Effective feature selection for Mars McMurdo image classification

  Shang, C., Barnes, D. & Shen, Q., 2009, 9th International Conference on Intelligent Systems Design and Applications, 2009. ISDA '09. IEEE Press, p. 1419-1424

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  • Image Classification 100%
  • Mars 50%
  • Images 33%
  • Feature Extraction 33%
  • Classification 33%
  7 Citations (Scopus)

• Effective Feature Selection for Mars McMurdo Terrain Image Classification

  Barnes, D. P., Shang, C. & Shen, Q., 2009, p. 1419-1424. 6 p.

  Research output: Contribution to conference › Paper

  File

  • Image Classification 100%
  • Feature Selection 66%
  • Feature Extraction 33%
  • Classification image 33%
  • Mars 33%
  169 Downloads (Pure)

• Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification

  Shang, C., Barnes, D. P. & Shen, Q., 2009, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Springer Nature, p. 209-216 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  File

  • Image Classification 100%
  • Mars 66%
  • Classification 66%
  • Application 33%
  • Utilization 33%
  1 Citation (Scopus)

  218 Downloads (Pure)
• 2008

  Aiding neural network based image classification with fuzzy-rough feature selection

  Shang, C. & Shen, Q., 2008, IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008. IEEE Press, p. 976-982 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  File

  • Feature Selection 100%
  • Classification (Machine Learning) 66%
  • Image Classification 33%
  • Complexity 33%
  • Classification Accuracy 33%
  2 Citations (Scopus)

  239 Downloads (Pure)
• 2006

  Aiding classification of gene expression data with feature selection: A comparative study

  Shen, Q. & Shang, C., 2006, In: Journal of Computational Intelligence Research (IJCIR). p. 68-76 9 p.

  Research output: Contribution to journal › Article › peer-review

  File

  • Feature Selection 100%
  • Classification 60%
  • Nearest Neighbour 40%
  • Gene Expression Data 40%
  • Information Gain 40%
  208 Downloads (Pure)