title: [Research on the application of pattern selection algorithm based on bioinformatic data].
abstract: Pattern selection plays an important role in data mining and pattern recognition, especially for large scale bioinformatic data. There are many problems in this field, such as algorithm complexity and numbers of the best feature subset. In this paper, we propose a new pattern selection algorithm, carrying out pattern selection base on Mutual Information (MI). Pattern subset evaluation index was studied to ensure the best feature subset. To pattern selection, algorithm bases on the correlation of patterns and label, as well as the redundancy of each pattern. Neurofuzzy Pattern Subset Evaluation Index was researched to make sure which is the best subset for our pattern subset evaluation. To verify the effectiveness of our method, several experiments are carried out on the data of gene expression of mouse from Leiden University and UCI datasets. The experimental results indicated that our algorithm achieved better results in the complexity and accuracy.
authors.affiliation: Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China. yddylixin@ysu.edu.cn
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.forename: Chun
authors.initials: C
authors.lastname: Zhao
authors.forename: Huihui
authors.initials: H
authors.lastname: Wang
authors.forename: Fangfang
authors.initials: F
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D017143:Fuzzy Logic; D006801:Humans; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods*