User:Jalayer masoud/sandbox

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Ensemble learning applications[edit]

In the recent years, due to the growing computational power which allows training large ensemble learning in a reasonable time frame, the number of its applications has been increasingly grown.[1] Some of the applications of ensemble classifiers include:

Remote sensing[edit]

Main article: Remote sensing

Land cover mapping[edit]

Land cover mapping is one of the major applications of Earth observation satellite sensors, using remote sensing and geospatial data, to identify the materials and objects which are located on the surface of target areas. Generally, the classes of target materials include roads, buildings, rivers, lakes, vegetation.[2] Some different ensembles based on artificial neural networks[3], kernel principal component analysis(KPCA)[4],decision trees with Boosting[5], random forest[6] and approaches for automatic designing of multiple classifier systems.[7]

Change detection[edit]

Change detection is an image analysis problem, consisting in the identification of places where the land cover has changed in time. Change detection is widely used in fields such as urban growth, forest and vegetation dynamics, land use and disaster monitoring.[8] The earliest applications of ensemble classifiers in change detection are designed with the majority voting, Bayesian average and the maximum posterior probability.[9]

Computer security[edit]

Distributed denial of service[edit]

Distributed denial of service is one of the most threatening cyber-attacks that may happens to an internet service provider.[1] By combining the the output of single classifiers, ensemble classifiers reduce the total error of detecting and discriminating such attacks from legitimate flash crowds.[10]

Malware Detection[edit]

Classification of malware codes such as computer viruses, computer worms, trojans, ransomware and spywares with usage of Machine Learning techniques is inspired by the document categorization problem.[11] Ensemble learning systems have showed a proper efficacy in this area.[12][13]

Intrusion detection[edit]

An Intrusion detection system monitors computer network or computer systems to identify intruder codes like an Anomaly detection process. Ensemble learning successfully aids such monitoring systems to reduce their total error.[14][15]

Face recognition[edit]

Main article: Face recognition

Face recognition, which recently has become one of the most popular research areas of pattern recognition, copes with identification or verification of a person by his/her digital images.[16] Hierarchial ensembles based on Gabor Fisher classifier and Independent component analysis preprocessing techniques are some of the earliest ensembles employed in this field.[17] [18] [19]

Emotion recognition[edit]

Main article: Emotion recognition

However speech recognition is mainly based on Deep Learning, as most of the industry players in this field like Google, Microsoft and IBM revealed that the core technology of their speech recognition is based on it, speech-based emotion recognition can have a satisfactory performance with ensemble learning.[20] [21] It also has being successfully used in Facial Emotion Recognition.[22][23] [24]

Fraud detection[edit]

Main article: Fraud detection

Fraud detection deals with the identification of bank fraud, such as money laundering, credit card fraud and telecommunication fraud, which have vast domains of research and applications of Machine Learning. Because ensemble learning improves the robustness of the normal behavior modelling, it has been proposed as an efficient technique to detect such fraudulent cases and activities in the banking and credit card systems.[25][26]

Financial decision-making[edit]

The accuracy of prediction of business failure is a very crucial issue in financial decision-making. Therefore some different ensemble classifiers are proposed to predict financial crises and financial distress.[27] Also, in the trade-based manipulation problem, where traders attempt to manipulate stock prices by buying and selling activities, ensemble classifiers are required to use the changes in the stock market data itself and then detect suspicious symptom of stock price manipulation.[28]

Medicine[edit]

Ensemble classifiers have been successfully applied to Neuroscience, Proteomics and medical diagnosis. Like in neuro-cognitive disorder (i.e. Alzheimer or myotonic dystrophy) detection based on MRI datasets[29][30] [31]

  1. ^ a b Woźniak, Michał; Graña, Manuel; Corchado, Emilio (March 2014). "A survey of multiple classifier systems as hybrid systems". Information Fusion. 16: 3–17. doi:10.1016/j.inffus.2013.04.006.
  2. ^ Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. (January 2012). "An assessment of the effectiveness of a random forest classifier for land-cover classification". ISPRS Journal of Photogrammetry and Remote Sensing. 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  3. ^ Giacinto, Giorgio; Roli, Fabio (August 2001). "Design of effective neural network ensembles for image classification purposes". Image and Vision Computing. 19 (9–10): 699–707. doi:10.1016/S0262-8856(01)00045-2.
  4. ^ Xia, Junshi; Yokoya, Naoto; Iwasaki, Yakira (March 2017). "A novel ensemble classifier of hyperspectral and LiDAR data using morphological features". 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 6185–6189. doi:10.1109/ICASSP.2017.7953345.
  5. ^ Mochizuki, S.; Murakami, T. (November 2012). "Accuracy comparison of land cover mapping using the object-oriented image classification with machine learning algorithms". 33rd Asian Conference on Remote Sensing 2012, ACRS 2012. 1: 126–133.
  6. ^ Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. (January 2012). "An assessment of the effectiveness of a random forest classifier for land-cover classification". ISPRS Journal of Photogrammetry and Remote Sensing. 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  7. ^ Giacinto, G.; Roli, F.; Fumera, G. (September 2000). "Design of effective multiple classifier systems by clustering of classifiers". Proceedings 15th International Conference on Pattern Recognition. ICPR-2000: 160–163. doi:10.1109/ICPR.2000.906039.
  8. ^ Du, Peijun; Liu, Sicong; Xia, Junshi; Zhao, Yindi (January 2013). "Information fusion techniques for change detection from multi-temporal remote sensing images". Information Fusion. 14 (1): 19–27. doi:10.1016/j.inffus.2012.05.003.
  9. ^ Bruzzone, Lorenzo; Cossu, Roberto; Vernazza, Gianni (December 2002). "Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images". Information Fusion. 3 (4): 289–297. doi:10.1016/S1566-2535(02)00091-X.
  10. ^ Raj Kumar, P. Arun; Selvakumar, S. (July 2011). "Distributed denial of service attack detection using an ensemble of neural classifier". Computer Communications. 34 (11): 1328–1341. doi:10.1016/j.comcom.2011.01.012.
  11. ^ Shabtai, Asaf; Moskovitch, Robert; Elovici, Yuval; Glezer, Chanan (February 2009). "Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey". Information Security Technical Report. 14 (1): 16–29. doi:10.1016/j.istr.2009.03.003.
  12. ^ Zhang, Boyun; Yin, Jianping; Hao, Jingbo; Zhang, Dingxing; Wang, Shulin (2007). "Malicious Codes Detection Based on Ensemble Learning". Autonomic and Trusted Computing: 468–477. doi:10.1007/978-3-540-73547-2_48.
  13. ^ Menahem, Eitan; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval (February 2009). "Improving malware detection by applying multi-inducer ensemble". Computational Statistics & Data Analysis. 53 (4): 1483–1494. doi:10.1016/j.csda.2008.10.015.
  14. ^ Locasto, Michael E.; Wang, Ke; Keromytis, Angeles D.; Salvatore, J. Stolfo (2005). "FLIPS: Hybrid Adaptive Intrusion Prevention". Recent Advances in Intrusion Detection: 82–101. doi:10.1007/11663812_5.
  15. ^ Giacinto, Giorgio; Perdisci, Roberto; Del Rio, Mauro; Roli, Fabio (January 2008). "Intrusion detection in computer networks by a modular ensemble of one-class classifiers". Information Fusion. 9 (1): 69–82. doi:10.1016/j.inffus.2006.10.002.
  16. ^ Mu, Xiaoyan; Lu, Jiangfeng; Watta, Paul; Hassoun, Mohamad H. (July 2009). "Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition". 2009 International Joint Conference on Neural Networks. doi:10.1109/IJCNN.2009.5178708.
  17. ^ Yu, Su; Shan, Shiguang; Chen, Xilin; Gao, Wen (April 2006). "Hierarchical ensemble of Gabor Fisher classifier for face recognition". Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on Automatic Face and Gesture Recognition (FGR06): 91–96. doi:10.1109/FGR.2006.64.
  18. ^ Su, Y.; Shan, S.; Chen, X.; Gao, W. (September 2006). "Patch-based gabor fisher classifier for face recognition". Proceedings - International Conference on Pattern Recognition. 2: 528–531. doi:10.1109/ICPR.2006.917.
  19. ^ Liu, Yang; Lin, Yongzheng; Chen, Yuehui (July 2008). "Ensemble Classification Based on ICA for Face Recognition". Proceedings - 1st International Congress on Image and Signal Processing, IEEE Conference, CISP 2008: 144–148. doi:10.1109/CISP.2008.581.
  20. ^ Rieger, Steven A.; Muraleedharan, Rajani; Ramachandran, Ravi P. (2014). "Speech based emotion recognition using spectral feature extraction and an ensemble of kNN classifiers". Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014: 589–593. doi:10.1109/ISCSLP.2014.6936711.
  21. ^ Krajewski, Jarek; Batliner, Anton; Kessel, Silke (October 2010). "Comparing Multiple Classifiers for Speech-Based Detection of Self-Confidence - A Pilot Study". 2010 20th International Conference on Pattern Recognition: 3716–3719. doi:10.1109/ICPR.2010.905.
  22. ^ Rani, P. Ithaya; Muneeswaran, K. (25 May 2016). "Recognize the facial emotion in video sequences using eye and mouth temporal Gabor features". Multimedia Tools and Applications. 76 (7): 10017–10040. doi:10.1007/s11042-016-3592-y.
  23. ^ Rani, P. Ithaya; Muneeswaran, K. (August 2016). "Facial Emotion Recognition Based on Eye and Mouth Regions". International Journal of Pattern Recognition and Artificial Intelligence. 30 (07): 1655020. doi:10.1142/S021800141655020X.
  24. ^ Rani, P. Ithaya; Muneeswaran, K (28 March 2018). "Emotion recognition based on facial components". Sādhanā. 43 (3). doi:10.1007/s12046-018-0801-6.
  25. ^ Louzada, Francisco; Ara, Anderson (October 2012). "Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool". Expert Systems with Applications. 39 (14): 11583–11592. doi:10.1016/j.eswa.2012.04.024.
  26. ^ Sundarkumar, G. Ganesh; Ravi, Vadlamani (January 2015). "A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance". Engineering Applications of Artificial Intelligence. 37: 368–377. doi:10.1016/j.engappai.2014.09.019.
  27. ^ Kim, Yoonseong; Sohn, So Young (August 2012). "Stock fraud detection using peer group analysis". Expert Systems with Applications. 39 (10): 8986–8992. doi:10.1016/j.eswa.2012.02.025.
  28. ^ Kim, Yoonseong; Sohn, So Young (August 2012). "Stock fraud detection using peer group analysis". Expert Systems with Applications. 39 (10): 8986–8992. doi:10.1016/j.eswa.2012.02.025.
  29. ^ Savio, A.; García-Sebastián, M.T.; Chyzyk, D.; Hernandez, C.; Graña, M.; Sistiaga, A.; López de Munain, A.; Villanúa, J. (August 2011). "Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI". Computers in Biology and Medicine. 41 (8): 600–610. doi:10.1016/j.compbiomed.2011.05.010.
  30. ^ Ayerdi, B.; Savio, A.; Graña, M. (June 2013). "Meta-ensembles of classifiers for Alzheimer's disease detection using independent ROI features". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Part 2): 122–130. doi:10.1007/978-3-642-38622-0_13.
  31. ^ Gu, Quan; Ding, Yong-Sheng; Zhang, Tong-Liang (April 2015). "An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology". Neurocomputing. 154: 110–118. doi:10.1016/j.neucom.2014.12.013.
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