|
[1]J. A. Langendijk, P. Doornaert, I. M. Verdonck-de Leeuw, C. R. Leemans, N. K. Aaronson, and B. J. Slotman, “Impact of late treatment-related toxicity on quality of life among patients with head and neck cancer treated with radiotherapy,” Journal of clinical oncology, vol. 26, no. 22, pp.3770-3776, 2008. [2]J. Grimm, D. Palma, S. Senan, and J. Xue, “Complication probability for radiation pneumonitis (RP) after stereotactic body radiotherapy (SBRT),” Journal of radiosurgery and SBRT, vol. 2, no. 2, p.99, 2013. [3]E. Huang, J. Bradley, I. El Naqa, M. Trovo, and J. Deasy, “TU‐C‐BRB‐08: Validating Normal Tissue Complication Probability Models: A Study of Generalizability and Datapooling for Predictive Radiation Pneumonitis Modeling,” Medical Physics, vol. 36, no. 6Part22, pp.2723-2723, 2009. [4]Y. Seppenwoolde, J. V. Lebesque, K. De Jaeger, J. S. Belderbos, L. J. Boersma, C. Schilstra, G. T. Henning, J. A. Hayman, M. K. Martel, and R. K. Ten Haken, “Comparing different NTCP models that predict the incidence of radiation pneumonitis,” International Journal of Radiation Oncology* Biology* Physics, vol. 55, no. 3, pp.724-735, 2003. [5]I. El Naqa, J. Bradley, A. I. Blanco, P. E. Lindsay, M. Vicic, A. Hope, and J. O. Deasy, “Multivariable modeling of radiotherapy outcomes, including dose–volume and clinical factors,” International Journal of Radiation Oncology* Biology* Physics, vol. 64, no. 4, pp.1275-1286, 2006. [6]J. M. Robertson, M. Söhn, and D. Yan, “Predicting grade 3 acute diarrhea during radiation therapy for rectal cancer using a cutoff-dose logistic regression normal tissue complication probability model,” International Journal of Radiation Oncology* Biology* Physics, vol. 77, no. 1, pp.66-72, 2010. [7]Z. John Lu, “The elements of statistical learning: data mining, inference, and prediction,” Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 173, no. 3, pp.693-694, 2010. [8]X. Li, and H. Zhao, “Weighted random subspace method for high dimensional data classification,” Statistics and its Interface, vol. 2, no. 2, p.153, 2009. [9]J. Peng, J. Zhu, A. Bergamaschi, W. Han, D.-Y. Noh, J. R. Pollack, and P. Wang, “Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer,” The annals of applied statistics, vol. 4, no. 1, p.53, 2010. [10]E. Senkus-Konefka, and J. Jassem, “Complications of breast-cancer radiotherapy,” Clinical Oncology, vol. 18, no. 3, pp.229-235, 2006. [11]J. C. Theuws, S. L. Kwa, A. C. Wagenaar, Y. Seppenwoolde, L. J. Boersma, E. M. Damen, S. H. Muller, P. Baas, and J. V. Lebesque, “Prediction of overall pulmonary function loss in relation to the 3-D dose distribution for patients with breast cancer and malignant lymphoma,” Radiotherapy and oncology, vol. 49, no. 3, pp.233-243, 1998. [12]E. B. C. T. C. Group, “Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials,” The Lancet, vol. 366, no. 9503, pp.2087-2106, 2005. [13]E. B. C. T. C. Group, “Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10 801 women in 17 randomised trials,” The Lancet, vol. 378, no. 9804, pp.1707-1716, 2011. [14]S. L. Kwa, J. V. Lebesque, J. C. Theuws, L. B. Marks, M. T. Munley, G. Bentel, D. Oetzel, U. Spahn, M. V. Graham, and R. E. Drzymala, “Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients,” International Journal of Radiation Oncology* Biology* Physics, vol. 42, no. 1, pp.1-9, 1998. [15]T.-F. Lee, P.-J. Chao, L. Chang, H.-M. Ting, and Y.-J. Huang, “Developing multivariable normal tissue complication probability model to predict the incidence of symptomatic radiation pneumonitis among breast cancer patients,” PloS one, vol. 10, no. 7, p.e0131736, 2015. [16]G. Gagliardi, J. Bjöhle, I. Lax, A. Ottolenghi, F. Eriksson, A. Liedberg, P. Lind, and L. E. Rutqvist, “Radiation pneumonitis after breast cancer irradiation: analysis of the complication probability using the relative seriality model,” International Journal of Radiation Oncology* Biology* Physics, vol. 46, no. 2, pp.373-381, 2000. [17]P. A. Lind, B. Wennberg, G. Gagliardi, S. Rosfors, U. Blom-Goldman, A. Lideståhl, and G. Svane, “ROC curves and evaluation of radiation-induced pulmonary toxicity in breast cancer,” International Journal of Radiation Oncology* Biology* Physics, vol. 64, no. 3, pp.765-770, 2006. [18]P. Tzionas, S. E. Papadakis, and D. Manolakis, "Plant leaves classification based on morphological features and a fuzzy surface selection technique," Fifth international conference on technology and automation, Thessaloniki, Greece, pp.365-370, 2005. [19]K.-C. Li, “Sliced inverse regression for dimension reduction,” Journal of the American Statistical Association, vol. 86, no. 414, pp.316-327, 1991. [20]Y. Xia, H. Tong, W. K. Li, and L. X. Zhu, “An adaptive estimation of dimension reduction space,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 64, no. 3, pp.363-410, 2002. [21]E. Morabito, and M. Versaci, “A fuzzy neural approach to localizing holes in conducting plates,” IEEE transactions on magnetics, vol. 37, no. 5, pp.3534-3537, 2001. [22]R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996. [23]Jheng-Sian Syu, 2017, “Using Least Absolute Shrinkage and Selection Operator and Mahalanobis-Taguchi System in Feature Selection to Improve the Predictive Ability of Radiation-Induced Complication in Breast Cancer,” National Kaohsiung University of Applied Sciences, Master's thesis. [24]C.-D. Tseng, C.-S. Shieh, Y.-J. Huang, P.-J. Chao, and T.-F. Lee, “Using LASSO regression based SVM classification to improve the predictive performance of radiation-induced pneumonitis complication in breast cancer,” Journal of the Chinese Institute of Engineers, vol. 41, no. 8, pp.660-666, 2018. [25]I. Tsamardinos, L. E. Brown, and C. F. Aliferis, “The max-min hill-climbing Bayesian network structure learning algorithm,” Machine learning, vol. 65, no. 1, pp.31-78, 2006. [26]J. G. Han, T. H. Park, Y. H. Moon, and I. K. Eom, “Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion,” Journal of Electronic Imaging, vol. 25, no. 2, p.023031, 2016. [27]H. Zhang, L. Ding, Y. Zou, S.-Q. Hu, H.-G. Huang, W.-B. Kong, and J. Zhang, “Predicting drug-induced liver injury in human with Naïve Bayes classifier approach,” Journal of computer-aided molecular design, vol. 30, no. 10, pp.889-898, 2016. [28]R. Harpaz, W. DuMouchel, P. LePendu, and N. H. Shah, "Empirical Bayes model to combine signals of adverse drug reactions," Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1339-1347, 2013. [29]P. Therasse, S. G. Arbuck, E. A. Eisenhauer, J. Wanders, R. S. Kaplan, L. Rubinstein, J. Verweij, M. Van Glabbeke, A. T. van Oosterom, and M. C. Christian, “New guidelines to evaluate the response to treatment in solid tumors,” Journal of the National Cancer Institute, vol. 92, no. 3, pp.205-216, 2000. [30]D.-C. Li, and I.-H. Wen, “A genetic algorithm-based virtual sample generation technique to improve small data set learning,” Neurocomputing, vol. 143, pp.222-230, 2014. [31]S. Cho, M. Jang, and S. Chang, “Virtual sample generation using a population of networks,” Neural Processing Letters, vol. 5, no. 2, pp.21-27, 1997. [32]D.-C. Li, and Y.-H. Fang, “A non-linearly virtual sample generation technique using group discovery and parametric equations of hypersphere,” Expert Systems with Applications, vol. 36, no. 1, pp.844-851, 2009. [33]Z.-S. Chen, B. Zhu, Y.-L. He, and L.-A. Yu, “A PSO based virtual sample generation method for small sample sets: Applications to regression datasets,” Engineering Applications of Artificial Intelligence, vol. 59, pp.236-243, 2017. [34]T.-F. Lee, P.-J. Chao, H.-M. Ting, L. Chang, Y.-J. Huang, J.-M. Wu, H.-Y. Wang, M.-F. Horng, C.-M. Chang, and J.-H. Lan, “Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of xerostomia after intensity-modulated radiotherapy for head and neck cancer,” PloS one, vol. 9, no. 2, p.e89700, 2014. [35]C.-J. Xu, A. van der Schaaf, A. A. Van't Veld, J. A. Langendijk, and C. Schilstra, “Statistical validation of normal tissue complication probability models,” International Journal of Radiation Oncology* Biology* Physics, vol. 84, no. 1, pp.e123-e129, 2012. [36]C.-J. Xu, A. van der Schaaf, C. Schilstra, J. A. Langendijk, and A. A. van’t Veld, “Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models,” International Journal of Radiation Oncology* Biology* Physics, vol. 82, no. 4, pp.e677-e684, 2012. [37]H. Trevor, T. Robert, and F. JH, “The elements of statistical learning: data mining, inference, and prediction,” The Mathematical Intelligencer, vol. 27, no. 2, pp.83-85, 2009. [38]F. Castelletti, G. Consonni, M. L. Della Vedova, and S. Peluso, “Learning Markov Equivalence Classes of Directed Acyclic Graphs: An Objective Bayes Approach,” Bayesian Analysis, vol. 13, no. 4, pp.1235-1260, 2018. [39]D. Heckerman, D. Geiger, and D. M. Chickering, “Learning Bayesian networks: The combination of knowledge and statistical data,” Machine learning, vol. 20, no. 3, pp.197-243, 1995. [40]N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian networks to analyze expression data,” Journal of computational biology, vol. 7, no. 3-4, pp.601-620, 2000. [41]I. Tsamardinos, C. F. Aliferis, A. R. Statnikov, and E. Statnikov, "Algorithms for Large Scale Markov Blanket Discovery," FLAIRS conference, pp.376-380, 2003. [42]S. Yaramakala, and D. Margaritis, "Speculative Markov blanket discovery for optimal feature selection," Data mining, fifth IEEE international conference on, pp.4-6, 2005. [43]M. G. Madden, “Evaluation of the performance of the markov blanket bayesian classifier algorithm,” arXiv preprint cs/0211003, pp.151-159, 2002. [44]R. Kohavi, and G. H. John, “Wrappers for feature subset selection,” Artificial intelligence, vol. 97, no. 1-2, pp.273-324, 1997. [45]L. Yu, and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” Journal of machine learning research, vol. 5, no. 4, pp.1205-1224, 2004. [46]N. Slonim, G. Bejerano, S. Fine, and N. Tishby, “Discriminative feature selection via multiclass variable memory Markov model,” EURASIP Journal on Applied Signal Processing, vol. 2003, pp.93-102, 2003. [47]M. Egmont-Petersen, "Feature Selection by Markov Chain Monte Carlo Sampling–A Bayesian Approach," Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp.1034-1042, 2004. [48]C. Cortes, and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp.273-297, 1995. [49]B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," Proceedings of the fifth annual workshop on Computational learning theory, pp.144-152, 1992. [50]I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine learning, vol. 46, no. 1-3, pp.389-422, 2002. [51]V. Vapnik, and S. Mukherjee, "Support vector method for multivariate density estimation," Advances in neural information processing systems, pp.659-665, 2000. [52]J. Huang, and C. X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Transactions on knowledge and Data Engineering, vol. 17, no. 3, pp.299-310, 2005. [53]C. X. Ling, J. Huang, and H. Zhang, "AUC: a better measure than accuracy in comparing learning algorithms," Conference of the canadian society for computational studies of intelligence, pp.329-341, 2003. [54]L. Gaudette, and N. Japkowicz, "Evaluation methods for ordinal classification," Canadian Conference on Artificial Intelligence, pp.207-210, 2009. [55]Z. Yu, and F. L. Tse, “An evaluation of numerical integration algorithms for the estimation of the area under the curve (AUC) in pharmacokinetic studies,” Biopharmaceutics & drug disposition, vol. 16, no. 1, pp.37-58, 1995. [56]A. A. El-Tahtawy, T. N. Tozer, F. Harrison, L. Lesko, and R. Williams, “Evaluation of bioequivalence of highly variable drugs using clinical trial simulations. II: Comparison of single and multiple-dose trials using AUC and Cmax,” Pharmaceutical research, vol. 15, no. 1, pp.98-104, 1998. [57]S. Wu, and P. Flach, "A scored AUC metric for classifier evaluation and selection," Second Workshop on ROC Analysis in ML, Bonn, Germany, pp.91-96, 2005. [58]A. T. Meadows, D. L. Friedman, J. P. Neglia, A. C. Mertens, S. S. Donaldson, M. Stovall, S. Hammond, Y. Yasui, and P. D. Inskip, “Second neoplasms in survivors of childhood cancer: findings from the Childhood Cancer Survivor Study cohort,” Journal of Clinical Oncology, vol. 27, no. 14, p.2356, 2009. [59]A. B. Mariotto, L. M. Schwartz, N. Howlader, and S. Woloshin, “Providing Clinicians and Patients With Actual Prognosis: Cancer in the Context of Competing Causes of Death,” JNCI Monographs, vol. 2014, no. 49, pp.255-264, 2014. [60]R. Yancik, “Ovarian cancer: age contrasts in incidence, histology, disease stage at diagnosis, and mortality,” Cancer, vol. 71, no. S2, pp.517-523, 1993. [61]L. A. G. Ries, “Ovarian cancer: survival and treatment differences by age,” Cancer, vol. 71, no. S2, pp.524-529, 1993. [62]M. F. Leitzmann, and S. Rohrmann, “Risk factors for the onset of prostatic cancer: age, location, and behavioral correlates,” Clinical epidemiology, vol. 4, p.1, 2012. [63]D.-C. Li, and Y.-S. Lin, “Using virtual sample generation to build up management knowledge in the early manufacturing stages,” European Journal of Operational Research, vol. 175, no. 1, pp.413-434, 2006. [64]S. Sun, R. Huang, and Y. Gao, “Network-scale traffic modeling and forecasting with graphical lasso and neural networks,” Journal of Transportation Engineering, vol. 138, no. 11, pp.1358-1367, 2012. [65]W. M. Van der Aalst, V. Rubin, H. Verbeek, B. F. van Dongen, E. Kindler, and C. W. Günther, “Process mining: a two-step approach to balance between underfitting and overfitting,” Software & Systems Modeling, vol. 9, no. 1, p.87, 2010. [66]X. Guyon, and J.-f. Yao, “On the underfitting and overfitting sets of models chosen by order selection criteria,” Journal of Multivariate Analysis, vol. 70, no. 2, pp.221-249, 1999. [67]Y. Gu, B. K. Wylie, S. P. Boyte, J. Picotte, D. M. Howard, K. Smith, and K. J. Nelson, “An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data,” Remote Sensing, vol. 8, no. 11, p.943, 2016. [68]P. G. Gibson, D. H. Bryant, G. W. Morgan, M. Yeates, V. Fernandez, R. Penny, and S. N. Breit, “Radiation-induced lung injury: a hypersensitivity pneumonitis?,” Annals of internal medicine, vol. 109, no. 4, pp.288-291, 1988. [69]I. Beetz, C. Schilstra, A. van der Schaaf, E. R. van den Heuvel, P. Doornaert, P. van Luijk, A. Vissink, B. F. van der Laan, C. R. Leemans, and H. P. Bijl, “NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors,” Radiotherapy and Oncology, vol. 105, no. 1, pp.101-106, 2012. [70]I. Beetz, C. Schilstra, F. R. Burlage, P. W. Koken, P. Doornaert, H. P. Bijl, O. Chouvalova, C. R. Leemans, G. H. de Bock, and M. E. Christianen, “Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva: the role of dosimetric and clinical factors,” Radiotherapy and Oncology, vol. 105, no. 1, pp.86-93, 2012. [71]S. D. Ramsey, M. R. Andersen, R. Etzioni, C. Moinpour, S. Peacock, A. Potosky, and N. Urban, “Quality of life in survivors of colorectal carcinoma,” Cancer, vol. 88, no. 6, pp.1294-1303, 2000. [72]F.-M. Fang, W.-L. Tsai, T.-F. Lee, K.-C. Liao, H.-C. Chen, and H.-C. Hsu, “Multivariate analysis of quality of life outcome for nasopharyngeal carcinoma patients after treatment,” Radiotherapy and Oncology, vol. 97, no. 2, pp.263-269, 2010. [73]M. Guckenberger, K. Baier, B. Polat, A. Richter, T. Krieger, J. Wilbert, G. Mueller, and M. Flentje, “Dose–response relationship for radiation-induced pneumonitis after pulmonary stereotactic body radiotherapy,” Radiotherapy and Oncology, vol. 97, no. 1, pp.65-70, 2010. [74]X. Ding, W. Ji, J. Li, X. Zhang, and L. Wang, “Radiation recall pneumonitis induced by chemotherapy after thoracic radiotherapy for lung cancer,” Radiation oncology, vol. 6, no. 1, p.24, 2011. [75]A. G. Taghian, S. I. Assaad, A. Niemierko, I. Kuter, J. Younger, R. Schoenthaler, M. Roche, and S. N. Powell, “Risk of pneumonitis in breast cancer patients treated with radiation therapy and combination chemotherapy with paclitaxel,” Journal of the National Cancer Institute, vol. 93, no. 23, pp.1806-1811, 2001. [76]Y. Segawa, N. Takigawa, M. Kataoka, I. Takata, N. Fujimoto, and H. Ueoka, “Risk factors for development of radiation pneumonitis following radiation therapy with or without chemotherapy for lung cancer,” International Journal of Radiation Oncology* Biology* Physics, vol. 39, no. 1, pp.91-98, 1997. [77]B. Parashar, A. Edwards, R. Mehta, M. Pasmantier, A. G. Wernicke, A. Sabbas, R. S. Kerestez, D. Nori, and K. C. Chao, “Chemotherapy significantly increases the risk of radiation pneumonitis in radiation therapy of advanced lung cancer,” American journal of clinical oncology, vol. 34, no. 2, pp.160-164, 2011. [78]T.-K. Yu, G. J. Whitman, H. D. Thames, A. U. Buzdar, E. A. Strom, G. H. Perkins, N. R. Schechter, M. D. McNeese, S.-W. Kau, and E. S. Thomas, “Clinically relevant pneumonitis after sequential paclitaxel-based chemotherapy and radiotherapy in breast cancer patients,” Journal of the National Cancer Institute, vol. 96, no. 22, pp.1676-1681, 2004. [79]J. G. Elmore, M. B. Barton, V. M. Moceri, S. Polk, P. J. Arena, and S. W. Fletcher, “Ten-Year Risk of False Positive Screening Mammograms and Clinical Breast Examinations,” New England Journal of Medicine, vol. 338, no. 16, pp.1089-1096, 1998. [80]L. Kessler, and A. Fox, “Screening mammography: a missed clinical opportunity,” JAMA, vol. 264, pp.54-58, 1990. [81]S. J. Winawer, R. H. Fletcher, L. Miller, F. Godlee, M. Stolar, C. Mulrow, S. Woolf, S. Glick, T. Ganiats, and J. Bond, “Colorectal cancer screening: clinical guidelines and rationale,” Gastroenterology, vol. 112, no. 2, pp.594-642, 1997. [82]J. Nikkilä, K. A. Coleman, D. Morrissey, K. Pylkäs, H. Erkko, T. E. Messick, S.-M. Karppinen, A. Amelina, R. Winqvist, and R. A. Greenberg, “Familial breast cancer screening reveals an alteration in the RAP80 UIM domain that impairs DNA damage response function,” Oncogene, vol. 28, no. 16, p.1843, 2009. [83]C. Taylor, C. Correa, F. K. Duane, M. C. Aznar, S. J. Anderson, J. Bergh, D. Dodwell, M. Ewertz, R. Gray, and R. Jagsi, “Estimating the risks of breast cancer radiotherapy: evidence from modern radiation doses to the lungs and heart and from previous randomized trials,” Journal of Clinical Oncology, vol. 35, no. 15, p.1641, 2017. [84]S. A. Kovalchik, M. Tammemagi, C. D. Berg, N. E. Caporaso, T. L. Riley, M. Korch, G. A. Silvestri, A. K. Chaturvedi, and H. A. Katki, “Targeting of low-dose CT screening according to the risk of lung-cancer death,” New England Journal of Medicine, vol. 369, no. 3, pp.245-254, 2013. [85]R. Manser, L. B. Irving, C. Stone, G. Byrnes, M. J. Abramson, and D. Campbell, “Screening for lung cancer,” Cochrane database of systematic reviews, vol. 10, no. 4, pp.266-271, 2004. [86]E. B. C. T. C. Group, “Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials,” The lancet, vol. 378, no. 9793, pp.771-784, 2011. [87]G. M. Reisfield, B. A. Goldberger, and R. L. Bertholf, “‘False-positive’and ‘false-negative’test results in clinical urine drug testing,” Bioanalysis, vol. 1, no. 5, pp.937-952, 2009. [88]J. C. Ton, and H. Z. Aeilko, “Clinical Trials are Often False Positive: A Review of Simple Methods to Control This Problem,” Current Clinical Pharmacology, vol. 1, no. 1, pp.1-4, 2006. [89]R. Roman, M. Sala, D. Salas, N. Ascunce, R. Zubizarreta, and X. Castells, “Effect of protocol-related variables and women's characteristics on the cumulative false-positive risk in breast cancer screening,” Annals of oncology, vol. 23, no. 1, pp.104-111, 2011. [90]A. Borsellino, A. Zaccara, A. Nahom, A. Trucchi, L. Aite, C. Giorlandino, and P. Bagolan, “False-positive rate in prenatal diagnosis of surgical anomalies,” Journal of pediatric surgery, vol. 41, no. 4, pp.826-829, 2006. [91]J. Madigan, Y. Rikihisa, J. Palmer, E. DeRock, and J. Mott, “Evidence for a high rate of false-positive results with the indirect fluorescent antibody test for Ehrlichia risticii antibody in horses,” Journal of the American Veterinary Medical Association, vol. 207, no. 11, pp.1448-1453, 1995. [92]N. Cesa-Bianchi, A. Conconi, and C. Gentile, “On the generalization ability of on-line learning algorithms,” IEEE Transactions on Information Theory, vol. 50, no. 9, pp.2050-2057, 2004. [93]M. T. Musavi, K. H. Chan, D. M. Hummels, and K. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 6, pp.659-663, 1994. [94]B. Hammer, “Generalization ability of folding networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 2, pp.196-206, 2001. [95]J. Huang, J. Lu, and C. X. Ling, "Comparing naive Bayes, decision trees, and SVM with AUC and accuracy," Third IEEE International Conference on Data Mining, pp.553-556, 2003. [96]T.-F. Lee, M.-H. Liou, Y.-J. Huang, P.-J. Chao, H.-M. Ting, H.-Y. Lee, and F.-M. Fang, “LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma,” Scientific reports, vol. 4, p.6217, 2014. [97]I. Beetz, C. Schilstra, P. van Luijk, M. E. Christianen, P. Doornaert, H. P. Bijl, O. Chouvalova, E. R. van den Heuvel, R. J. Steenbakkers, and J. A. Langendijk, “External validation of three dimensional conformal radiotherapy based NTCP models for patient-rated xerostomia and sticky saliva among patients treated with intensity modulated radiotherapy,” Radiotherapy and Oncology, vol. 105, no. 1, pp.94-100, 2012. [98]F. Chauchard, R. Cogdill, S. Roussel, J. Roger, and V. Bellon-Maurel, “Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes,” Chemometrics and Intelligent Laboratory Systems, vol. 71, no. 2, pp.141-150, 2004. [99]C. Jose, P. Goyal, P. Aggrwal, and M. Varma, "Local deep kernel learning for efficient non-linear svm prediction," International conference on machine learning, pp.486-494, 2013. [100]F. Nie, Y. Huang, X. Wang, and H. Huang, "New primal SVM solver with linear computational cost for big data classifications," Proceedings of the 31st International Conference on International Conference on Machine Learning, pp.II-505, 2014. [101]T. Imam, K. M. Ting, and J. Kamruzzaman, "z-SVM: An SVM for improved classification of imbalanced data," Australasian Joint Conference on Artificial Intelligence, pp.264-273, 2006. [102]W. Feng, Q. Zhang, G. Hu, and J. X. Huang, “Mining network data for intrusion detection through combining SVMs with ant colony networks,” Future Generation Computer Systems, vol. 37, pp.127-140, 2014. [103]K. Sivakami, and N. Saraswathi, “Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model,” International Journal of Scientific Engineering and Applied Science (IJSEAS), vol. 1, no. 5, pp.418-429, 2015. [104]Q. Wu, and D.-X. Zhou, “SVM soft margin classifiers: linear programming versus quadratic programming,” Neural computation, vol. 17, no. 5, pp.1160-1187, 2005. [105]C.-L. Huang, and J.-F. Dun, “A distributed PSO–SVM hybrid system with feature selection and parameter optimization,” Applied soft computing, vol. 8, no. 4, pp.1381-1391, 2008. [106]S. Han, C. Qubo, and H. Meng, "Parameter selection in SVM with RBF kernel function," World Automation Congress (WAC), pp.1-4, 2012. [107]R. Caruana, and D. Freitag, "Greedy attribute selection," Machine Learning Proceedings 1994, pp. 28-36: Elsevier, 1994. [108]E. Raczko, and B. Zagajewski, “Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images,” European Journal of Remote Sensing, vol. 50, no. 1, pp.144-154, 2017. [109]Y. Shao, and R. S. Lunetta, “Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 70, pp.78-87, 2012. [110]S. K. Das, S. Chen, J. O. Deasy, S. Zhou, F. F. Yin, and L. B. Marks, “Combining multiple models to generate consensus: Application to radiation‐induced pneumonitis prediction,” Medical physics, vol. 35, no. 11, pp.5098-5109, 2008. [111]C. Dehing-Oberije, D. De Ruysscher, A. van Baardwijk, S. Yu, B. Rao, and P. Lambin, “The importance of patient characteristics for the prediction of radiation-induced lung toxicity,” Radiotherapy and Oncology, vol. 91, no. 3, pp.421-426, 2009. [112]T. W. Schiller, Y. Chen, I. El Naqa, and J. O. Deasy, “Modeling radiation-induced lung injury risk with an ensemble of support vector machines,” Neurocomputing, vol. 73, no. 10-12, pp.1861-1867, 2010. [113]Q. Cheng, H. Zhou, and J. Cheng, “The fisher-markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 6, pp.1217-1233, 2011. [114]S.-Z. Yu, “Hidden semi-Markov models,” Artificial intelligence, vol. 174, no. 2, pp.215-243, 2010. [115]M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing, and M. Sugiyama, “High-dimensional feature selection by feature-wise kernelized lasso,” Neural computation, vol. 26, no. 1, pp.185-207, 2014. [116]C. F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, “Local causal and markov blanket induction for causal discovery and feature selection for classification part ii: Analysis and extensions,” Journal of Machine Learning Research, vol. 11, no. Jan, pp.235-284, 2010.
|