关键词: 随机森林, 分类器, 抗性基因, 聚类, 降采样

Abstract: The traditional homology sequence alignment based approaches usually have high false positive rate and consequently new resistance genes are difficult to be identified. This paper presents a resistance gene identification approach by applying random forest classifier and K-Means under-sampling method. In order to solve the aimless problem in gene-mining research, two main contributions are provided. Firstly, it introduces random forest and 188 dimension features to identify resistance genes, accordingly the sample statistic learning approach can efficiently capture the internal characteristic of resistance genes. Secondly, it selects a more representative training subset and reduces the identification errors for solving the serious imbalanced classification during the training process. The experimental results indicate that the approach can efficiently identify the resistance genes, not only precisely clas-sifying the existing experimental verified data, but also obtaining high accuracy on the negative sample dataset.

Key words: random forest, classifier, resistance gene, cluster, under-sampling