Abstract
Artificial Immune System (AIS) is excited by organic and biological immune system. These systems are exceptionally intelligent and computationally accurate. Thus, they are able to handle massive amount of data in a very short interval of time. The chapter initially proposes the algorithm and then describes the implementation of the proposed model for anomaly detection on the Wisconsin cancer dataset. The elemental idea behind this research is to develop an AIS, which generates and procreates antibodies that can successfully ascertain the behavior of the system and detect congenital and foreign anomalies (antigens). A new computational intelligence approach has been adopted to diagnose and improve the accuracy of the model. Various statistical and machine learning techniques like gradient descent, non-negative matrix factorization, cosine similarity rule, \(L_2\) optimization reduce the complexity of a model. This results in training the model faster and reduces overfitting.
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The authors would like to acknowledge L&T Infotech funding under CSR-1Step initiative.
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Keni, N., Suradkar, N., Dixit, M., Siddavatam, I.A., Kazi, F. (2019). A Computational Intelligence Approach for Cancer Detection Using Artificial Immune System. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_36
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DOI: https://doi.org/10.1007/978-981-13-1132-1_36
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