The Feature Compression Algorithms for Identifying Cytokines Based on CNT Features

As the signaling proteins, cytokines regulate a wide range of biological functions. It is important to distinguish the cytokines from other kinds of proteins. The 188-Dimensional CNT features are presented to identify the cytokines, which contain many redundant features. In this paper, we propose three kinds of feature compression algorithms to exclude the redundant features from the 188D features and keep the accuracy of the algorithm at the same time. The three algorithms are called the genetic based algorithm, the greedy based algorithm and the brute-force based algorithm. Experimental results demonstrate that the brute-force based algorithm gets the highest classification accuracy among the three algorithms. The genetic based algorithm achieves the least number of compressed features among the three algorithms. But they consume much more time than that consumed by the greedy based algorithm. The greedy based algorithm makes a good trade-off among the three factors, which are the classification accuracy, the number of compressed features and the time consumption.