Abstract
This paper discusses the classification of microarray data for breast cancer gene expressions using a Genetic Algorithm. The available CuMiDa dataset is investigated regarding its suitability for Machine Learning (ML) applications as well as presenting the benchmark scores of a collection of selected ML algorithms. The methodology and use of a Genetic Algorithm (GA) both as a classifier and for feature pre-selection is explored and compared with hybrid or fusion architectures. Finally, an ensemble setup of a GA with a Support Vector Machine (SVM) is implemented with a subset of features. It is compared to a simple SVM on the whole feature set with the result that it is able to match it in performance across all applied metrics, although just a relatively small number (10–20) from the total number of features (36,000) is used.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Daoud M, Mayo M (2019) A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med 97:204–214. https://doi.org/10.1016/j.artmed.2019.01.006
Feltes BC, Chandelier EB, Grisci BI, Dorn M (2019) CuMiDa: an extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research. J Comput Biol 26(4):376–386. https://doi.org/10.1089/cmb.2018.0238
Hengpraprohm S, Mukviboonchai S, Thammasang R, Chongstitvatana P (2010) A GA-based classifier for microarray data classification. In: 2010 International conference on intelligent computing and cognitive informatics. IEEE, pp 199–202. https://doi.org/10.1109/ICI-CCI.2010.62
Alomari OA, Khader AT, Al-Betar MA, Alkareem Alyasseri ZA (2018) A hybrid filter-wrapper gene selection method for cancer classification. In: 2018 2nd international conference on biosignal analysis, processing and systems (ICBAPS). IEEE, pp 113–118. https://doi.org/10.1109/ICBAPS.2018.8527392
Singh P, Shukla A, Vardhan M (2017) Hybrid approach for gene selection and classification using filter and genetic algorithm. In: 2017 international conference on inventive computing and informatics (ICICI). IEEE, pp 832–837. https://doi.org/10.1109/ICICI.2017.8365253
Ahmed E, El-Gayar N, El-Azab IA (2010) Support vector machine ensembles using features distribution among subsets for enhancing microarray data classification. In: 2010 10th international conference on intelligent systems design and applications. IEEE, pp 1242–1246. https://doi.org/10.1109/ISDA.2010.5687078
Gad AF (2021) PyGAD: an intuitive genetic algorithm python library. https://doi.org/10.48550/arxiv.2106.06158
Pedregosa F et al (2012) Scikit-learn: machine learning in python. J Mach Learn Res 12:28252830. https://doi.org/10.48550/arxiv.1201.0490
Yang XS, Deb S, Hanne T, He X (2019) Attraction and diffusion in nature-inspired optimization algorithms. Neural Comput Appl 31(7):1987–1994
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Plagemann, T., Dornberger, R., Hanne, T. (2023). Combining Genetic Algorithm and Support Vector Machine for Classification of Cancer on Microarray Data. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_45
Download citation
DOI: https://doi.org/10.1007/978-981-19-9858-4_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9857-7
Online ISBN: 978-981-19-9858-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)