Affiliations: [a]
College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China
| [b] Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang, Henan, China
Correspondence:
[*]
Corresponding author. Yun Wang, College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China. E-mail: [email protected].
Abstract: For those key data in feature genes selection which the neighborhood of a sample is not completely contained in its decision equivalence class, most of existing models lack of advantages. Therefore, in this paper, we propose a new model to handle this problem: fuzzy neighborhood conditional entropy model. First, fuzzy neighborhood granule and fuzzy decision-making are introduced by using a parameterized fuzzy similarity relation between samples to depict the gene expression profile data more accurately. Then, we introduce the both into conditional entropy and propose the definitions of fuzzy neighborhood conditional entropy, whose strict proof of the monotonicity and other theorems are given. The strategy, which combines algebra definition with information theory definition about the importance of feature genes subset, makes the measurement mechanism more perfect. In the meantime, we set parameters and discuss the importance of its selection to tolerate the noise in the data. Finally, we employ the monotonicity principle of fuzzy neighborhood conditional entropy to evaluate the significance of a candidate feature gene, using which a feature genes selection algorithm is designed for proposed model. Comparing with the existing related algorithms through data sets selected from the public data sources, the experimental results show that the proposed algorithm selects relatively few feature genes and possess higher classification performance.
