Abstract
In this paper, we are proposing new ensemble strategy for classification of lung nodules based on their malignancy ratings. The procedure we followed is simpler. In the first step, we construct different homogenous ensemble models such as bagged decision tree (BaDT), boosted decision tree (BoBT), and random subspace–based decision tree (RSSDT). In the next step, we combine previously constructed models with voting scheme to yield ensemble of homogenous ensemble of classifiers. We also examine the behavior of our method for heterogeneity in the system. This is done by constructing ensemble of heterogeneous ensemble of classifiers. For this, we have also considered bagged KNN (BaKNN), boosted KNN (BoKNN), bagged PART (BaPART), and boosted PART classifier (BoPART). The results we are obtaining from our strategy are significant compared to homogenous ensemble model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lung Image Database Consortium (LIDC) available at http://ncia.nci.nih.gov
Varutbangkul E, Mitrovic V, Aichu D, Furst J (2008) Combining boundaries abd rating from multiple observers for predicting lung nodule characteristics. In: IEEE international conference on biocomputing, bioinformatics and biomedical technologies, pp 82–87
Ebadollahi S, Johnson DE (2008) Diao M. Retrieving clinical cases through a concept space representation of text and images. SPIE Med, Imaging Symp
Nakumura K, Yoshida H, Engelmann R. MacMahon H, Kasturagawa S, Ishida T, et al (2000) Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology, pp 823–30
Kuncheva LI, Rodr′ıguez JJ, Plumpton CO, Linden DEJ, Johnston SJ (2010) Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging 29(2):531–542
Zinovev D, Raicu D, Furst J, Armato SG (2009) Predicting radiological panel opinions using a panel of machine learning classifiers. Algorithms 2:1473–1502. doi:10.3390/a2041473
Vinay K, Rao A, Hemantha Kumar G Comparative study on performance of single classifier with ensemble of classifiers in predicting radiological experts ratings on lung nodules. Indian international conference on artificial intelligence (IICAI-11). ISBN: 978-0-9727412-8-6, pp 393–403
Vinay K, Rao A, Hemantha Kumar G Computerized analysis of classification of lung nodules and comparison between homogeneous and heterogeneous ensemble of classifier model. 2011 third national conference on computer vision, pattern recognition, image processing and graphics, 978-0-7695-4599-8/11, IEEE doi:10.1109/NCVPRIPG.2011.56, pp 231–234
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Inc, Chichester
J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(3):226–239
L. Breiman. Bagging predictors. Technical Report 421, Department of Statistics, University of California, Berkeley, (1994)
Freund Y, Schapire RE (1997) A decision–theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
Ho TK (1998) The random space method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Vinay, K., Rao, A., Hemanthakumar, G. (2014). Combining Ensemble of Classifiers Using Voting-Based Rule to Predict Radiological Ratings for Lung Nodule Malignancy. In: Sridhar, V., Sheshadri, H., Padma, M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_45
Download citation
DOI: https://doi.org/10.1007/978-81-322-1157-0_45
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1156-3
Online ISBN: 978-81-322-1157-0
eBook Packages: EngineeringEngineering (R0)