Hybrid Preprocessing Method for Support Vector Machine for Classification of Imbalanced Cerebral Infarction Datasets
Abstract
Keywords
Full Text:
PDFReferences
V.Bay, B.F.Kjolby, N.K.Iversen et al., “Stroke Infarct Volume Estimation in Fixed Tissue : Comparison of Diffusion Kurtosis Imaging to Diffusion Weighted Imaging and Histology in a Rodent MCAO Modelâ€, PLoS ONE, vol. 13, no.4, e0196161, 2018.
G.Wang, J.Jing, Y.Pan, et al., “Does All Single Infarction have Lower Risk of Stroke Reccurence Than Multiple Infarctions in Minor Stroke?â€, BMC Neurology, vol. 19, no.7, 2019.
I.A.Mentari, R.Naufalina, M.Rahmadi, J.Khotib, “Development of Ischemic Stroke Model By Right Unilateral Common Carotid Artery Occlusion (RUCCAO) Methodâ€, Fol Med Indones, vol.54, no.3, pp.200-206, 2018.
M.F.Kabir, S.A.Ludwing, “Classification of Breast Cancer Risk Factors Using Several Resampling Approachesâ€, 17th IEEE International Conference on Machine Learning and Applications, 2018.
J.Burez, D.Van den Poel, “Handling Class Imbalanced in Customer Churn Predictionâ€, Expert Systems with Applications, vol.36, no.3, pp.4626-4636, 2009.
A. Amin, S. Anwar, A. Adnan et al., “Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case studyâ€, IEEE Access, vol. 4, pp. 7940-7957, 2016.
T.Vafeiadis, K.I.Diamantaras, G. Sarigiannidis, K.C.Chatzisavvas, “A Comparison of Machine Learning Techniques for Customer Churn Predictionâ€, Simulation Modelling Practice and Theory, vol.55, pp.1-9, 2015.
M.Buda, A.Maki, M.A.Mazurowski, “A Systematic Study of The Class Imbalance Problem in Convolutional Neural Networkâ€, Neural Network, vol. 106, pp. 249-259, 2018.
H.He, E.A.Garcia, “Learning from Imbalanced Dataâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.21, No.9, 2009.
D.S.Sisodia, U.Verma, “The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Modelsâ€, International Journal on Electrical Engineering and Informatics, vol.10, no. 2, 2018.
J.Luengo, A. Fernandez, S.Garcia, F.Herrera, “Addresing Data Complexity for Imbalanced Data Sets :Analysis of SMOTE-based Oversampling and Evolutionary Undersamplingâ€, Soft Comput, vol. 15, pp.1909-1936, 2018.
U.R.Salunkhe, S.N.Mali, “Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction : A Novel Extension to Under-Samplingâ€, I.J.Intelligent Systems and Applications, vol.5, pp.71-81, 2018.
H.Guo, X.Diao, H.Liu, “Embedding Undersampling Rotation Forest for Imbalanced Problemâ€, Hindawi Computational Intelligence and Neuroscience, 2018.
J.Liu, E.Zio, “Integration of Feature Vector Selection and Support Vector Machine for Classification of Imbalanced Dataâ€, Applied Soft Computing Journal vol.75, pp. 702-711, 2017.
R.Batuwita, V.Palade, “Class Imbalance Learing Methods for Support Vector Machinesâ€.
DOI: http://dx.doi.org/10.18517/ijaseit.9.2.8615
Refbacks
- There are currently no refbacks.
Published by INSIGHT - Indonesian Society for Knowledge and Human Development