Comparison of machine learning techniques for target detection

Artificial Intelligence Review

Volumn 43, Issue 1, 2015, Pages 125-139

  Vink, Jelte Peter ^a   de Haan, Gerard ^a  

^a PHILIPS RESEARCH LABORATORIES   (Netherlands)

Author keywords

AdaBoost; Classifier design; Machine learning; Performance evaluation; Supervised learning; SVM

Indexed keywords

ADAPTIVE BOOSTING; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

CLASSIFIER DESIGN; COMPARATIVE STUDIES; DETECTION TIME; MACHINE LEARNING TECHNIQUES; MULTIPLE ORDERS; PERFORMANCE EVALUATION; REAL-TIME DETECTION; TRAINING TIME;

LEARNING SYSTEMS;

EID: 84922001788     PISSN: 02692821     EISSN: 15737462     Source Type: Journal    
DOI: 10.1007/s10462-012-9366-7     Document Type: Article

References (59)

1. Amasyali MF, Ersoy OK (2011) Comparison of single and ensemble classifiers in terms of accuracy and execution time. In: International symposium on innovation intelligent system applications. doi:10.1109/INISTA.2011.5946119
2. Aruna S, Rajagopalan SP, Nandakishore LV (2011) An empirical comparison of supervised learning algorithms in disease detection. Int J Inf Technol Converg Serv 1: 81–92
3. Bartlett MS, Littlewort G, Lainscsek C, Fasel I, Movellan J (2004) Machine learning methods for fully automatic recognition of facial expressions and facial actions. In: Proceedings of the IEEE international conference systems, man and cybernetics, pp 592–597
4. Breiman L (1999) Prediction games and arcing algorithms. Neural Comput 11(7):1493–1517. doi:10.1162/089976699300016106
5. Breiman L (2001) Random forests. Mach Learn 45(1): 5–32
6. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Statistics/ probability series. Wadsworth Publishing Company, Belmont
7. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167. doi:10.1023/A:1009715923555. http://dl.acm.org/citation.cfm?id=593419.593463
8. Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning, pp 161–168. doi:10.1145/1143844.1143865
9. Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th international conference on machine learning, pp 96–103. doi:10.1145/1390156.1390169
10. Che D, Hockenbury C, Marmelstein R, Rasheed K (2010) Classification of genomic islands using decision trees and their ensemble algorithms. BMC Genomics 11(Suppl 2):S1. doi:10.1186/1471-2164-11-S2-S1
11. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273–297
12. Dettling M, Bühlmann P (2003) Boosting for tumor classification with gene expression data. Bioinforma 19(9):1061–1069. http://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics19.htmlDettlingB03
13. Doshi F, Brunskill E, Shkolnik A, Kollar T, Rohanimanesh K, Tedrake R, Roy N (2007) Collision detection in legged locomotion using supervised learning. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems
14. Douglas PK, Harris S, Yuille A, Cohen MS (2011)Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. NeuroImage 56(2):544–553. doi:10.1016/j.neuroimage.2010.11.002
15. Enzweiler M, Gavrila DM (2009) Monocular pedestrian detection: survey and experiments. IEEE Trans Pattern Analysis Mach Intell 31(12):2179–2195. doi:10.1109/TPAMI.2008.260
16. Flexer A (1994) Statistical evaluation of neural network experiments: minimum requirements and current practice. Aust Res Inst Artif Intell 2: 1005–1008
17. Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
18. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139. doi:10.1006/jcss.1997.1504. http://portal.acm.org/citation.cfm?id=261540.261549
19. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200): 675–701. doi:10.2307/2279372
20. Friedman J, Hastie T, Tibshirani R (1998) Additive logistic regression: a statistical view of boosting. Ann Stat 28: 2000
21. García S, Herrera F (2008) An extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694. http://www.jmlr.org/papers/volume9/garcia08a/garcia08a.pdf
22. Graf ABA., Smola AJ., Borer S (2003) Classification in a normalized feature space using support vector machines. IEEE Trans Neural Netw 14(3): 597–605. doi:10.1109/TNN.2003.811708
23. Guyon I., Hur AB., Gunn S., Dror G (2004) Result analysis of the nips 2003 feature selection challenge. Adv Neural Inf Process Syst 17: 545–552
24. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1): 10–18. doi:10.1145/1656274.1656278
25. Ho YC, Pepyne DL (2002) Simple explanation of the no free lunch theorem of optimization. Cybern Syst Anal 38(2):4409–4414. http://www.springerlink.com/index/T1Q45BRR3TNU1K1R.pdf
26. Hume D, Hendel C (1955) An Inquiry concerning human understanding: with a supplement, an abstract of a treatise of human nature. Library of liberal arts, Bobbs-Merrill. http://books.google.nl/books?id=-P4HAQAAIAAJ
27. Iman RL, Davenport JM (1980) Approximations of the critical region of the friedman statistic. Commun Stat 9: 571–595
28. Joachims T (1999) Making large-scale support vector machine learning practical. MIT Press, Cambridge, pp 169–184. http://dl.acm.org/citation.cfm?id=299094.299104
29. Khan R, Hanbury A, Stöttinger J (2010) Skin detection: a random forest approach. IEEE Int Conf Image Process 4613–4616
30. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31(3):249–268. http://www.informatica.si/PDF/31-3/11_Kotsiantis
31. Krishnaraj Y., Reddy CK (2008) Boosting methods for protein fold recognition: an empirical comparison. IEEE Int Conf Bioinform Biomed 8: 393–396. doi:10.1109/BIBM.2008.83
32. Lin H, Li L (2005) Infinite ensemble learning with support vector machines. In: Proceedings of the 16th European conference on machine learning, pp 242–254
33. Lloyd SP (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28: 129–137
34. Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Struct 405(2):442–451. doi:10.1016/0005-2795(75)90109-9. http://www.sciencedirect.com/science/article/pii/0005279575901099
35. McDonald RA, Hand DJ, Eckley IA (2003) An empirical comparison of three boosting algorithms on real data sets with artificial class noise. In: Proceedings of the 4th international conference multiple classification system, pp 35–44. http://dl.acm.org/citation.cfm?id=1764295.1764301
36. Mease D, Wyner A (2008) Evidence contrary to the statistical view of boosting. J Mach Learn Res 9:131–156. http://dl.acm.org/citation.cfm?id=1390681.1390687
37. Mitchell TM (1980) The need for biases in learning generalizations. Technical Report CBM-TR-117, Department of Computer Science, Rutgers University
38. Miyamoto D, Hazeyama H, Kadobayashi Y (2008) An evaluation of machine learning-based methods for detection of phishing sites. Aus J Intell Inf Process Syst 10(2): 54–63
39. Mooney RJ (1996) Comparative experiments on disambiguating word senses: an illustration of the role of bias in machine learning. In: Proceedings of the conference on Empire methods in national language processing, pp 82–91. http://www.cs.utexas.edu/users/ai-lab/pub-view.php?PubID=51464
40. Prechelt L (1996) A quantitative study of experimental evaluations of neural network learning algorithms: current research practice. Neural Netw 9(3):457–462. doi:10.1016/0893-6080(95)00123-9. http://www.sciencedirect.com/science/article/pii/0893608095001239
41. Quddus A, Fieguth P, Basir O (2005) Adaboost and support vector machines for white matter lesion segmentation in mr images. In: 27th Annual international conference on engineering in medicine and biology society, pp 463–466. doi:10.1109/IEMBS.2005.1616447
42. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco
43. Ridgeway G (1999) The state of boosting. Comput Sci Stat 31: 172–181
44. Rodríguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Analysis Mach Intell 28(10): 1619–1630
45. Rojas-Bello RN, Lago-Fernández LF, Martínez-Muñoz G, Sánchez-Montañ és MA (2011) A comparison of techniques for robust gender recognition. IEEE Int Conf Image Process 569–572
46. Salzberg S (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min Knowl Discov 1: 317–327
47. Salzberg SL (1999) On comparing classifiers: a critique of current research and methods. Data Min Knowl Discov 1: 1–12
48. Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human Pose Recognition in Parts from Single Depth Images. Comput Vis Pattern Recogn. doi:10.1109/CVPR.2011.5995316. http://research.microsoft.com/apps/pubs/default.aspx?id=145347
49. Statnikov A, Aliferis CF (2007) Are random forests better than support vector machines for microarray-based cancer classification? In: AMIA annual symposium proceedings, pp 686–690. http://view.ncbi.nlm.nih.gov/pubmed/18693924
50. Statnikov A, Tsamardinos I, Dosbayev Y, Aliferis CF (2005) GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int J Med Inform 74(7–8):491–503. doi:10.1016/j.ijmedinf.2005.05.002
51. Statnikov A, Wang L, Aliferis C (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinforma 9(1):319. doi:10.1186/1471-2105-9-319
52. Tang Y, Krasser S, He Y, Yang W, Alperovitch D (2008) Support vector machines and random forests modeling for spam senders behavior analysis. GLOBECOM pp 2174–2178. http://dblp.uni-trier.de/db/conf/globecom/globecom2008.htmlTangKHYA08
53. Vink JP, de Haan G (2011) No-reference metric design with machine learning for local video compression artifact level. IEEE J Sel Top Signal Process 5(2): 297–308. doi:10.1109/JSTSP.2010.2055832
54. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2): 137–154. doi:10.1023/B:VISI.0000013087.49260.fb
55. Wang Y, Han P, Lu X, Wu R, Huang J (2006) The performance comparison of adaboost and svm applied to sar atr. CIE international conference on radar, pp 1–4. doi:10.1109/ICR.2006.343515
56. Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159–196
57. Wolpert DH (1992) On the connection between in-sample testing and generalization error. Complex Syst 6: 47–94
58. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. doi:10.1109/4235.585893
59. Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2007) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37. doi:10.1007/s10115-007-0114-2