Disciplinary: Computer Engineering, Biomedical Technology.
Keywords: Artificial Intelligence, Medical Imaging, Object-Based Classification, Support Vector Machine (SVM), WEKA; AdaBoost SVM Classifier.
In cell-based research, an effective classification approach is required for visually monitoring a large quantity of image data of cells in vitro treatment. It is important to classify alive and dead cells likewise in tumor cell images, detecting virus-cell images, etc. to analyze patients' situation and then provide patient-centered care. Traditionally, the classification methods employed for classifying the cell microscopy data is time-consuming and is susceptible to faults and delusion. This is a serious and crucial dilemma. Accurate classification of data set is a major task in cell-based research as it determines the treatment. This paper introduces a hybrid model that uses a nonlinear HSIC Lasso feature selection method combined with the AdaBoost SVM Classifier to classify a large quantity of data effectively and efficiently. In the proposed model, object-based classification is executed within the bounds of the Waikato Environment for Knowledge Analysis (WEKA) interface. Besides, the accuracy of the classifier is evaluated by methods like feature selection and interactive learning in WEKA. The performance comparison of the proposed model amid existing classification approaches proved that the method is better in minimizing the mean absolute error value successfully.
Paper ID: 12A1G
Cite this article:
Madanan, M., Venugopal, A. (2021). Designing a Hybrid Model Using HSIC Lasso Feature Selection and Adaboost Classifier to Classify Image Data in Biomedicine. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(1), 12A1G, 1-14. http://doi.org/10.14456/ITJEMAST.2021.7
References
-
Baatz, M., Arini, N., Schape, A., Binnig, G., & Linssen, B. (2006). Object-oriented image analysis for high content screening: Detailed quantification of cells and subcellular structures with the Cellenger software. International Society for Analytical Cytology, 652-658.
-
Bouckaert, R. R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., & Scuse, D. (2008). Weka manual for version 3-6-0. Hamilton: University of Waikato.
-
Chen, S., Zhao, M., Wu, G., Yao, C., & Zhang, J. (2012). Recent Advances in Morphological Cell Image Analysis. Computational and mathematical methods in medicine.
-
Chengsheng, T., Huacheng, L., & Bing, X. (2017). AdaBoost typical Algorithm and its application research. MATEC Web of Conferences, EDP Sciences.
-
Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. International Workshop on Multiple Classifier Systems (pp. 1-15). Berlin Heidelberg: Springer.
-
Ding, C., & Peng, H. (2003). Minimum redundancy feature selection from microarray gene expression data. Computational Systems Bioinformatics. CSB2003, Proceedings of the 2003 IEEE Bioinformatics Conference (pp. 523-528). Stanford: IEEE.
-
Fan, Z.-G., Wang, K.-A., & Lu, B.-L. (2004). Feature selection for fast image classification with support vector machines. International Conference on Neural Information Processing, (pp. 1026-1031). Berlin, Heidelberg: Springer.
-
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I., & Trigg, L. (2009). Weka-A machine learning workbench for data mining. In O. Maimon, & L. Rokach, The Data Mining and Knowledge Discovery Handbook (pp. 1269-1277). Boston: Springer.
-
Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 42-47.
-
Gauraha, N. (2018). Introduction to the LASSO: A Convex Optimization Approach for High-dimensional Problems. Resonance, 439-464.
-
Hand, D., & Christen, P. (2018). A note on using the F-measure for evaluating record linkage algorithms. Statistics and Computing, 539-547.
-
Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval, and Pervasive (pp. 3-24). ACM-Digital Library.
-
Li, C., Xue, D., Hu, Z., Chen, Y. H., Yao, U., Zhang, Y., Xu, N. (2019). A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. International Conference on Information Technologies in Biomedicine (pp. 222-233). Springer.
-
Liu, D., & Xia, F. (2010). Assessing object-based classification: advantages. Remote Sensing Letters, 187-194.
-
Panca, V., & Rustam, Z. (2017). Application of machine learning on brain cancer multiclass classification. International Symposium on Current Progress in Mathematics and Sciences 2016 (ISCPMS 2016), Indonesia, AIP Publishing.
-
Pavlov, D., Mao, J., & Dom, B. (2002). Scaling-up support vector machines using a boosting algorithm. International Conference on Pattern Recognition ICPR. Spain: IEEE Xplore.
-
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1226-1238.
-
Peng, Y., Wu, Z., & Jiang, J. (2010). A novel feature selection approach for biomedical data classification. Journal of Biomedical Informatics, 15-23.
-
Popescu, M. C., & Sasu, L. M. (2014). Feature extraction, feature selection and machine learning for image classification: A case study. International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). Bran, Romania: IEEE.
-
Ravikumar, P., Lafferty, J., Liu, H., & Wasserman, L. (2009). Sparse Additive Models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1009-1030.
-
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
-
Takahashi, Y., Uek, M., Yamada, M., Tamiya, G., Motoike, I. N., Saigusa, D., . . . Tomita, H. (2020). Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection. Translational psychiatry, 1-12.
-
Theriault, D. H., Walker, M. L., Wong, J. Y., & Betke, M. (2012). Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning. Machine Vision and Applications, 659-673.
-
Tibshirani, R. (2014). Regression shrinkage and selection via the lasso: A Retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 273-282.
-
Wang, X., Zhang, X., Zeng, Z., QunWu, & JianZhang. (2016). Unsupervised spectral feature selection with l1-norm graph. Neurocomputing, 47-54.
-
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2017). Deep learning in microscopy image analysis: A survey. IEEE Transactions on Neural Networks and Learning Systems, 4550-4569.
-
Yamada, M., Tang, J., Lugo-Martinez, J., Hodzic, E., Shrestha, R., Saha, A., . . . Yin, D. (2018). Ultra high-dimensional nonlinear feature selection for big biological data. IEEE Transactions on Knowledge and Data Engineering, 1352-1365.
-
Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. Proceedings of the Twentieth International Conference (ICML), USA.
-
Zhang, B.-T., & Hwang, K.-B. (2003). Bayesian network classifiers for gene expression analysis. A Practical Approach to Microarray Data Analysis, 150-165.
-
Zhang, Y., Guo, W., & Ray, S. (2016). On the consistency of feature selection with lasso for non-linear targets. Proceedings of The 33rd International Conference on Machine Learning, (pp. 183-191). ML Research Press.
Other issues:
Vol.11(16)(2021)
Vol.11(15)(2020)
Vol.11(14)(2020)
Vol.11(13)(2020)
Vol.11(12)(2020)
Vol.11(11)(2020)
Vol.11(10)(2020)
Vol.11(9)(2020)
Vol.11(8)(2020)
Vol.11(7)(2020)
Vol.11(6)(2020)
Vol.11(5)(2020)
Vol.11(4)(2020)
Vol.11(3)(2020)
Vol.11(2)(2020)
Vol.11(1)(2020)
Archives
Call-for-Papers
Call-for-Scientific Papers
Call-for-Research Papers: ITJEMAST invites you to submit high quality papers for full peer-review and possible publication in areas pertaining engineering, science, management and technology, especially interdisciplinary/cross-disciplinary/multidisciplinary subjects.
To publish your work in the next available issue, your manuscripts together with copyright transfer document signed by all authors can be submitted via email to Editor @ TuEngr.com (no space between). (please see all detail from
Instructions for Authors)
Publication and peer-reviewed process:
After the peer-review process (4-10 weeks), articles will be on-line published in the available next issue. However, the
International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies cannot guarantee the exact publication time as the process may take longer time, subject to peer-review approval and adjustment of the submitted articles.