L1-Regulated Feature Selection in Microarray Cancer Data and Classification Using Random Forest Tree
Microarray cancer data are characterized by high dimensionality, small sample size, noisy data, and an imbalanced number of samples among classes. To alleviate this challenge, several machine learning-oriented techniques are proposed by authors from several disciplines such as computer science, computational biology, statistics, and pattern recognition. In this work, we propose L1-regulated feature selection method and classification of microarray cancer data using Random Forest tree classifier. The experiment is conducted on eight standard microarray cancer datasets. We explore the learning curve of the model, which indicates the learning capability of the classifier from a different portion of the training samples. To overcome the overfitting problem, feature scaling is carried out before the actual training takes place and the learning curve is explored using fivefold cross-validation method during the actual training time. Comparative analysis is carried out with state-of-the-art work, and the proposed method outperforms many of the recently published works in the domain. Evaluation of the proposed method is carried out using several performance evaluation techniques such as classification accuracy, recall, precision, f-measure, area under the curve, and confusion matrix.
KeywordsMicroarray cancer Learning curve L1-regulated feature selection Random Forest tree Classification Learning curve
- 1.Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2002). Cancer as a micro evolutionary processGoogle Scholar
- 2.Sharbaf, F. V., Mosafer, S., & Moattar, M. H. (2016). A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics, 107(6), 231–238.Google Scholar
- 7.Medjahed, S. A., Saadi, T. A., Benyettou, A., & Ouali, M. (2017). Kernel-based learning and feature selection analysis for cancer diagnosis. Applied Soft Computing, 51, 39–48.Google Scholar
- 8.Liu, Z., Tang, D., Cai, Y., Wang, R., & Chen, F. (2017). A hybrid method based on ensemble WELM for handling multi class imbalance in cancer microarray data. Neurocomputing.Google Scholar
- 9.Farid, D. M., Al-Mamun, M. A., Manderick, B., & Nowe, A. (2016). An adaptive rule-based classifier for mining big biological data. Expert Systems with Applications, 64, 305–316.Google Scholar
- 10.García, V., & Sánchez, J. S. (2015). Mapping microarray gene expression data into dissimilarity spaces for tumor classification. Information Sciences, 294, 362–375.Google Scholar
- 14.Tsamardinos, I.,. Statnikov, A., Aliferis, C. F.: Gene expression model selector. (Online). Available: http://www.gems-system.org/.
- 15.Andres Cano, S. M., & Masegosa, A. Elvira biomedical data set repository (Online). Available: http://leo.ugr.es/elvira/DBCRepository/.
- 16.Hess, K. R., & Wei, C. (2010). Learning curves in classification with microarray data. Seminars in Oncology, 37(1) (Elsevier).Google Scholar
- 17.Dashtban, M., Balafar, M., & Suravajhala, P. (2018). Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics, 110(1), 10–17.Google Scholar
- 18.Dash, R. (2018). An adaptive harmony search approach for gene selection and classification of high dimensional medical data. Journal of King Saud University-Computer and Information Sciences.Google Scholar
- 22.Kumar, M., Singh, S., & Rath, S. K. (2015). Classification of microarray data using functional link neural network. Procedia Computer Science, 57, 727–737. Google Scholar