*4.1. Data Acquisition*

Table 2 summarizes the 24 datasets from the UCI machine learning repository [59] that were used in the experiments. The datasets were selected with different instances and attribute numbers to represent various kinds of issue (small, medium and large). In each repository, the instances are divided randomly into three different subsets, namely training, testing, and validation subsets. The proposed algorithms were tested over three gene expression datasets of colon cancer, lymphoma and the leukemia [64–66]. The K-NN is used in the experimental tests using the trial and error method, and 5 is the best choice of K. Meanwhile, every position of whale produces one attribute subset through the training process. The training set is used to test and evaluate the performance of the K-NN classifier in the validation subset throughout the optimization process. The bWOA is employed to simultaneously guide the FS process.


**Table 2.** List of datasets used in the experiments results.
