یک روش ترکیبی برای یافتن زیرمجموعه ویژگی مؤثر در داده‌های چند برچسبی

[1] Q. Luo, E. Chen, and H. Xiong, "A semantic term weighting scheme for text categorization," Expert Systems with Applications, vol. 38, pp. 12708-12716, 2011.

[2] K. Trohidis, G. Tsoumakas, G. Kalliris, and I. P. Vlahavas, "Multi-Label Classification of Music into Emotions," in ISMIR, pp. 325-330, 2008.

[3] J. Yang, Y.-G. Jiang, A. G. Hauptmann, and C.-W. Ngo, "Evaluating bag-of-visual-words representations in scene classification," in Proceedings of the international workshop on Workshop on multimedia information retrieval, pp. 197-206, 2007.

[4] M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, "Learning multi-label scene classification," Pattern recognition, vol. 37, pp. 1757-1771, 2004.

[5] S. Diplaris, G. Tsoumakas, P. A. Mitkas, and I. Vlahavas, "Protein classification with multiple algorithms," in Panhellenic Conference on Informatics, pp. 448-456, 2005.

[6] M.-L. Zhang and Z.-H. Zhou, "Multilabel neural networks with applications to functional genomics and text categorization," IEEE transactions on Knowledge and Data Engineering, vol. 18, pp. 1338-1351, 2006.

[7] S. Kashef and H. Nezamabadi-pour, "A new feature selection algorithm based on binary ant colony optimization," in Information and Knowledge Technology (IKT), 2013 5th Conference on, pp. 50-54, 2013.

[8] فاطمه علیقارداشی و محمدعلی زارع چاهوکی, "تأثیر ترکیب روش‌های انتخاب ویژگی فیلتر و بسته‌بندی در بهبود پیش‌بینی اشکال نرم‌افزار," مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 1، صفحات 183 تا 195، بهار 1396.

[9] شیما کاشف و حسین نظام‌آبادی‌پور, "ارائه یک نسخه جدید از الگوریتم مورچگان باینری به منظور حل مسأله انتخاب ویژگی," نشریه مهندسی برق و کامپیوتر ایران، دوره 12، شماره 2,صفحات 127 تا 144، زمستان 1393.

[10] S. Kashef and H. Nezamabadi-pour, "An advanced ACO algorithm for feature subset selection," Neurocomputing, vol. 147, pp. 271-279, 2015.

[11] حامد توحیدی, حسین نظام‌آبادی‌پور و س. سریزدی, "انتخاب ویژگی با استفاده از الگوریتم جمعیت مورچگان باینری," اولین کنگره مشترک سیستمهای فازی و هوشمند، مشهد، ایران, 1386.

[12] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "BGSA: binary gravitational search algorithm," Natural Computing, vol. 9, pp. 727-745, 2010.

[13] L.-Y. Chuang, S.-W. Tsai, and C.-H. Yang, "Improved binary particle swarm optimization using catfish effect for feature selection," Expert Systems with Applications, vol. 38, pp. 12699-12707, 2011.

[14] N. SpolaôR, E. A. Cherman, M. C. Monard, and H. D. Lee, "A comparison of multi-label feature selection methods using the problem transformation approach," Electronic Notes in Theoretical Computer Science, vol. 292, pp. 135-151, 2013.

[15] M.-L. Zhang, J. M. Peña, and V. Robles, "Feature selection for multi-label naive Bayes classification," Information Sciences, vol. 179, pp. 3218-3229, 2009.

[16] M.-L. Zhang and Z.-H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern recognition, vol. 40, pp. 2038-2048, 2007.

[17] F. De Comité, R. Gilleron, and M. Tommasi, "Learning multi-label alternating decision trees from texts and data," in International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 35-49, 2003.

[18] E. Spyromitros, G. Tsoumakas, and I. Vlahavas, "An empirical study of lazy multilabel classification algorithms," in Hellenic conference on Artificial Intelligence, pp. 401-406, 2008.

[19] L. Zhang, Q. Hu, J. Duan, and X. Wang, "Multi-label feature selection with fuzzy rough sets," in International Conference on Rough Sets and Knowledge Technology, pp. 121-128, 2014.

[20] G. Doquire and M. Verleysen, "Feature selection for multi-label classification problems," in International Work-Conference on Artificial Neural Networks, pp. 9-16, 2011.

[21] J. Read, B. Pfahringer, and G. Holmes, "Multi-label classification using ensembles of pruned sets," in 2008 Eighth IEEE International Conference on Data Mining, pp. 995-1000, 2008.

[22] O. Reyes, C. Morell, and S. Ventura, "Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context," Neurocomputing, vol. 161, pp. 168-182, 2015.

[23] J. Lee and D.-W. Kim, "Feature selection for multi-label classification using multivariate mutual information," Pattern Recognition Letters, vol. 34, pp. 349-357, 2013.

[24] O. Reyes, C. Morell, and S. Ventura, "ReliefF-ML: an extension of reliefF algorithm to multi-label learning," in Iberoamerican Congress on Pattern Recognition, pp. 528-535, 2013.

[25] N. SpolaôR, E. A. Cherman, M. C. Monard, and H. D. Lee, "relief for multi-label feature selection," IEEE Brazilian Conference on Intelligent Systems (BRACIS), pp. 6-11, 2013.

[26] J. Lee and D.-W. Kim, "Memetic feature selection algorithm for multi-label classification," Information Sciences, vol. 293, pp. 80-96, 2015.

[27] Y. Lin, Q. Hu, J. Liu, and J. Duan, "Multi-label feature selection based on max-dependency and min-redundancy," Neurocomputing, vol. 168, pp. 92-103, 2015.

[28] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions on pattern analysis and machine intelligence, vol. 27, pp. 1226-1238, 2005.

[29] J. Yin, T. Tao, and J. Xu, "A Multi-label feature selection algorithm based on multi-objective optimization," in Neural Networks (IJCNN), 2015 International Joint Conference on, pp. 1-7, 2015.

[30] N. Spolaôr, M. C. Monard, G. Tsoumakas, and H. D. Lee, "A systematic review of multi-label feature selection and a new method based on label construction," Neurocomputing, vol. 180, pp. 3-15, 2016.

[31] H. Lim, J. Lee, and D.-W. Kim, "Optimization approach for feature selection in multi-label classification," Pattern Recognition Letters, vol. 89, pp. 25-30, 2017.

[32] J. Lee and D.-W. Kim, "SCLS: Multi-label feature selection based on scalable criterion for large label set," Pattern Recognition, 2017.

[33] L. Qiao, L. Zhang, Z. Sun, and X. Liu, "Selecting label-dependent features for multi-label classification," Neurocomputing, 2017.

[34] L. Yu and H. Liu, "Feature selection for high-dimensional data: A fast correlation-based filter solution," in ICML, pp. 856-863, 2003.

[35] J. Biesiada and W. Duch, "Feature selection for high-dimensional data—a Pearson redundancy based filter," in Computer Recognition Systems 2, ed: Springer, pp. 242-249, 2007.

[36] C. G. Weng and J. Poon, "A new evaluation measure for imbalanced datasets," in Proceedings of the 7th Australasian Data Mining Conference-Volume 87, pp. 27-32, 2008.