| Summary: | Cancer prediction has been shown to be important in the cancer research area. This importance has prompted many researchers to review machine learning-approaches to predict cancer outcome using gene expression dataset. This dataset consists of many genes (features) which can mislead the prediction ability of the machine learning methods, as some features may lead to confusion or inaccurate classification. Since finding the most informative genes for cancer prediction is challenging, feature selection techniques are recommended to pick important and relevant features out of large and complex datasets. In this research, we propose the Cuckoo search method as a feature selection algorithm, guided by the memory-based mechanism to save the most informative features that are identified by the best solutions. The purpose of the memory is to keep track of the selected features at every iteration and find the features that enhance classification accuracy. The suggested algorithm has been contrasted with the original algorithm using microarray datasets and the proposed algorithm has been shown to produce good results as compared to original and contemporary algorithms.
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