Abstract
This paper provides a framework for non-linearly classifying microarray dataset to determine the existence of malignant neoplasm in the patient’s sample set. A \( m \times n \) microarray technology is used to represent the sample data of the patients. Our model aims at predicting the class label of an unknown new sample that enters into the system during the runtime. With the help of microarray data, we have collaboratively applied game theory approaches along with computational methods to solve the problem. An apodictic approach for the construction of an optimized RFE (Recursive Feature Elimination) feature selection model using Dualist Algorithm is incorporated. Furthermore, the optimized features are subjected to a non-linear classification using decision tree, k-nearest neighbor and logistic regression on Wisconsin Breast cancer dataset. The simulations carried out using the above techniques have proved Dualist algorithm with RFE combined with four different linear classifier models like logistic regression, k-nearest neighbor, decision tree and random forest to be a better choice for classification.
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Vijayeeta, P., Das, M.N., Mishra, B.S.P. (2020). A Non-linear Approach for Classifying Malignant Neoplasm Using Dualist Optimization Algorithm. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_49
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DOI: https://doi.org/10.1007/978-981-13-8676-3_49
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