Publications with keyword "Classification"
1. | H. Jiang, P. Cao, M. Xu, J. Yang, O. Zaiane. "Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction ". Computers in Biology and Medicine, 127, December 2020. |
2. | N. Sood, L. Bindra, O. Zaiane. "Bi-Level Associative Classifier using Automatic Learning on Rules". International Conference on Database and Expert Systems Applications (DEXA), (ed: Sven Hartmann, Josef Küng, Gabriele Kotsis, A Min Tjoa, Ismail Khalil), pp 201-216, September 2020. |
3. | D. Gross, I. Steenstra, F. Harrell, C. Bellinger, O. Zaiane. "Machine Learning for Work Disability Prevention: Introduction to the Special Series". Journal of Occupational Rehabilitation, 30, pp 303-307, July 2020. |
4. | P. Cao, Z. Teng, M. Huang, J. Yang, D. Zhao, A. Trabelsi, O. Zaiane. "An ensemble framework with l21-norm regularized hypergraph laplacian multi-label learning for clinical data prediction". International Workshop on Biomedical and Health Informatics, (ed: Illhoi Yoo, Jinbo Bi, Xiaohua Hu), pp 1436-1442, November 2019. |
5. | J. Serrano-Lomelin, C. Nielsen, M. Jabbar, O. Wine, C. Bellinger, P. Villeneuve, D. Stieb, N. Aelicks, K. Aziz, I. Buka, S. Chandra, S. Crawford, P. Demers, A. Erickson, P. Hystad, M. Kumar, E. Phipps, P. Shah, Y. Yuan, O. Zaiane, A. Osornio-Vargas. " Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes". Environment International Journal, 131(13), pp 1-7, October 2019. |
6. | C. Bellinger, S. Sharma, N. Japkowicz, O. Zaiane. "Framework for Extreme Imbalance Classification: SWIM: Sampling With the Majority Class". Knowledge and Information Systems, 62(3), pp 841-866, May 2019. |
7. | S. Sharma, C. Bellinger, B. Krawczyk, N. Japkowicz, O. Zaiane. "Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance". IEEE International Conference on Data Mining (ICDM), Singapore, November 2018. |
8. | J. Li, O. Zaiane. " Exploiting Statistically Significant Dependent Rules for Associative Classification". Intelligent Data Analysis, 21(5), pp 1155-1172, July 2017. |
9. | A. Yaddolahi, A. Shahraki, O. Zaiane. " Current State of Text Sentiment Analysis from Opinion to Emotion Mining". ACM Computing Surveys, 50(2), pp 1-25, 33, May 2017. |
10. | M. Gheiratmand, I. Rish, G. Cecchi, M. Brown, R. Greiner, P. Bashivan, P. Polosecki, S. Dursun. "Learning Discriminative Functional Network Features of Schizophrenia". SPIE Medical Imaging, pp 101371A, April 2017. |
11. | R. Vega. "The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data". MSc Thesis, University of Alberta, January 2017. |
12. | F. Ahmed, M. Samorani, C. Bellinger, O. Zaiane. "Advantage of Integration in Big Data: Feature Generation in Multi-Relational Databases for Imbalanced Learning ". IEEE International Conference on Big Data, Washington, USA, December 2016. |
13. | P. Cao, X. Liu, D. Zhao, O. Zaiane. "Cost sensitive Ranking Support Vector Machine for Multi-label Data Learning". International Conference on Hybrid Intelligent Systems, Marrakech, Morocco, November 2016. |
14. | P. Cao, X. Liu, D. Zhao, O. Zaiane. "Sparse learning and hybrid probabilistic oversampling for Alzheimers Disease diagnosis". International Conference on Hybrid Intelligent Systems, Marrakech, Morocco, November 2016. |
15. | R. Ramasubbu, M. Brown, F. Cortese, I. Gaxiola, A. Greenshaw, S. Dursun, B. Goodyear, R. Greiner. "Accuracy of Automated Classification of Major Depressive Disorder as a Function of Symptom Severity". NeuroImage: Clinical, 12, pp 320-331, July 2016. |
16. | P. Cao, D. Zhao, O. Zaiane. "Hybrid probabilistic sampling with random subspace for imbalanced data learning". Intelligent Data Analysis: An International Journal, 18(6), pp 1089-1108, November 2014. |
17. | K. Golmohammadi, O. Zaiane, D. Diaz. "Detecting Stock Market Manipulation using Supervised Learning Algorithms ". International Conference on Data Science and Advanced Analytics, Shanghai, China, October 2014. |
18. | S. Ghiassian, R. Greiner, M. Brown, P. Jin. "Learning to Classify Psychiatric Disorders based on fMR Images: Autism vs Healthy and ADHD vs Healthy". Proceedings of the Workshop on Machine Learning and Interpretation in Neuroimaging, pp n/a, December 2013. |
19. | P. Cao, D. Zhao, O. Zaiane. "A PSO-based Cost-Sensitive Neural Network for Imbalanced Data Classification". Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), April 2013. |
20. | C. Thapa, O. Zaiane, D. Rafiei, A. Sharma. "Classifying Websites into Non-topical Categories". International Conference on Big Data Analytics and Knowledge Discovery (DAWAK), Vienna, Austria, (ed: Alfredo Cuzzocrea, Umeshwar Dayal), pp 364-377, September 2012. |
21. | Y. Liu, Z. Guo, X. Ke, O. Zaiane. "Protein Subcellular Localization Prediction with Associative Classification and Multi-class SVM". ACM Conference on Bioinformatics, Computational Biology and Biomedicine, Chicago, United States, pp 493-495, August 2011. |
22. | M. Hooshsadat, H. Samuel, S. Patel, O. Zaiane. "Fastest Association Rule Mining Algorithm Predictor - FARM-AP". International C* Conference on Computer Science , Montreal, Canada, pp 43-50, May 2011. |
23. | A. Foss, O. Zaiane. " Class Separation through Variance: A new Application of Outlier Detection". Knowledge and Information Systems, 29(3), pp 565-596, November 2010. |
24. | O. Zaiane, K. Deng. "An Occurrence Based Approach to Mine Emerging Sequences". International Conference on Big Data Analytics and Knowledge Discovery (DAWAK), pp 275-284, September 2010. |
25. | F. Mirzazadeh. "Using SNP Data to Predict Radiation Toxicity for Prostate Cancer Patients". MSc Thesis, University of Alberta, February 2010. |
26. | A. Foss, O. Zaiane, S. Zilles. "Unsupervised Class Separation of Multivariate Data through Cumulative Variance-based Ranking". IEEE International Conference on Data Mining (ICDM), Miami, USA, pp 139-148, December 2009. |
27. | K. Deng, O. Zaiane. "Contrasting Sequence Groups by Emerging Sequences". Discovery Science, Porto, Portugal, pp 377-384, October 2009. |
28. | C. Lee. "Modeling Spatial Correlations for Effective Discriminative Classifiers". PhD Thesis, January 2009. |
29. | D. Chodos, O. Zaiane. "ARC-UI: A Visualization Tool for Associative Classifiers". Information Visualization, London, England, pp 296-301, July 2008. |
30. | D. Chodos, O. Zaiane. "ARC-UI: A Visualization Tool for Associative Classifiers". Information Visualization, London, England, July 2008. |
31. | A. Srivastava, O. Zaiane, M. Antonie. "Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification". International Database Engineering and Applications Symposium, Banff, Canada, pp 141-148, September 2007. |
32. | A. Kapoor, R. Greiner. "Learning and Classifying under Hard Budgets". European Conference on Machine Learning (ECML), Porto, Portugal, pp 166-173, October 2005. |
33. | O. Zaiane, M. Antonie, A. Coman. "Mammography Classification by an Association Rule-Based Classifier". International ACM SIGKDD Workshop on Multimedia Data Mining, Springer Verlag, pp 62-69, July 2002. |
34. | O. Zaiane, M. Antonie. "Classifying text documents by associating terms with text categories". Australasian Database Conference, Melbourne, Australia, pp 215-222, February 2002. |
35. | M. Antonie, O. Zaiane, A. Coman. "Application of Data Mining Techniques for Medical Image Classification". International ACM SIGKDD Workshop on Multimedia Data Mining, pp 94-101, August 2001. |
36. | R. Greiner, A. Grove, D. Schuurmans. "On Learning Hierarchical Classifications". Value of Information in Inference, Learning and Decision-Making, January 1997. |
37. | R. Greiner, A. Grove, D. Roth. "Learning Active Classifiers". International Conference on Machine Learning (ICML), pp 207-215, July 1996. |
38. | R. Holte. "Very Simple Classification Rules Perform Well on Most Commonly Used Datasets". Machine Learning Journal (MLJ), 11, pp 63-91, January 1993. |