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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. view
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. PDFview
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. PDFview
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. PDFview
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. view
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. view
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. PDFview
8. J. Li, O. Zaiane. " Exploiting Statistically Significant Dependent Rules for Associative Classification". Intelligent Data Analysis, 21(5), pp 1155-1172, July 2017. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. view
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
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. PDFview
25. F. Mirzazadeh. "Using SNP Data to Predict Radiation Toxicity for Prostate Cancer Patients". MSc Thesis, University of Alberta, February 2010. PDFview
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. PDFview
27. K. Deng, O. Zaiane. "Contrasting Sequence Groups by Emerging Sequences". Discovery Science, Porto, Portugal, pp 377-384, October 2009. PDFview
28. C. Lee. "Modeling Spatial Correlations for Effective Discriminative Classifiers". PhD Thesis, January 2009. PDFview
29. D. Chodos, O. Zaiane. "ARC-UI: A Visualization Tool for Associative Classifiers". Information Visualization, London, England, pp 296-301, July 2008. PDFview
30. D. Chodos, O. Zaiane. "ARC-UI: A Visualization Tool for Associative Classifiers". Information Visualization, London, England, July 2008. view
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. PDFview
32. A. Kapoor, R. Greiner. "Learning and Classifying under Hard Budgets". European Conference on Machine Learning (ECML), Porto, Portugal, pp 166-173, October 2005. PDFview
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. PDFview
34. O. Zaiane, M. Antonie. "Classifying text documents by associating terms with text categories". Australasian Database Conference, Melbourne, Australia, pp 215-222, February 2002. PDFview
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. PDFview
36. R. Greiner, A. Grove, D. Schuurmans. "On Learning Hierarchical Classifications". Value of Information in Inference, Learning and Decision-Making, January 1997. view
37. R. Greiner, A. Grove, D. Roth. "Learning Active Classifiers". International Conference on Machine Learning (ICML), pp 207-215, July 1996. PDFview
38. R. Holte. "Very Simple Classification Rules Perform Well on Most Commonly Used Datasets". Machine Learning Journal (MLJ), 11, pp 63-91, January 1993. PSview
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