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Cancer detection is one of the key research topics in the medical field.Accurate detection of different cancer types is valuable in providing bettertreatment facilities and risk minimization for patients. This paper deals withthe classification problem of human cancer diseases by using gene expressiondata. It is presented a new methodology to analyze microarray datasets andefficiently classify cancer diseases. The new method first employs Signal toNoise Ratio (SNR) to find a list of a small subset of non-redundant genes.Then, after normalization, it is used Multi-Objective Particle SwarmOptimization (MOPSO) for feature selection and employed Adaptive K-NearestNeighborhood (KNN) for cancer disease classification. This method improves theclassification accuracy of cancer classification by reducing the number offeatures. The proposed methodology is evaluated by classifying cancer diseasesin five cancer datasets. The results are compared with the most recentapproaches, which increases the classification accuracy in each dataset.

Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood