Abstract
Classification of cancers according to the exact place of origin in the body is an important research problem that is being addressed both clinically and computationally. Application of data science and machine learning to a vast volume of imaging and genomics data regarding cancers has enabled computational researchers to accurately classify the tumor samples according to their place of origin. In current work, we developed a method to classify tumors using partial least squares regression as feature selector and support vector
machine classifiers. We have evaluated our approach on three cancer gene expression datasets and found classification accuracies of 100% in some circumstances. The comparison is conducted with standard classification methods like Decision Trees and simple Support Vector Machines with respect to standard performance parameters and the time taken for classification. The comparison in terms of training and testing accuracies and the time taken for classification results show that our method performs consistently better than conventional
methods.
machine classifiers. We have evaluated our approach on three cancer gene expression datasets and found classification accuracies of 100% in some circumstances. The comparison is conducted with standard classification methods like Decision Trees and simple Support Vector Machines with respect to standard performance parameters and the time taken for classification. The comparison in terms of training and testing accuracies and the time taken for classification results show that our method performs consistently better than conventional
methods.
Original language | English |
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Title of host publication | 2022 International Conference for Advancement in Technology (ICONAT) |
Publisher | IEEE |
ISBN (Electronic) | 978-1-6654-2577-3 |
ISBN (Print) | 978-1-6654-2578-0 |
DOIs | |
Publication status | Published (in print/issue) - 10 Mar 2022 |
Event | IEEE International Conference for Advancement in Technology - , India Duration: 20 Jan 2022 → 21 Jan 2022 |
Publication series
Name | 2022 International Conference for Advancement in Technology (ICONAT) |
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Publisher | IEEE |
Conference
Conference | IEEE International Conference for Advancement in Technology |
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Country/Territory | India |
Period | 20/01/22 → 21/01/22 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT Authors thank the Department of Science and Technology (DST), Government of India, for financially supporting this work under the DST-ICPS grant scheme for the year 2018.
Publisher Copyright:
© 2022 IEEE.
Keywords
- Classification
- Machine Learning
- Support Vector Machines
- Regression Analysis