CAJ | 학술논문

　　对肿瘤基因表达谱进行分析，从而有效区分正常样本与肿瘤样本的关键是：准确找出能够决定样本类别的最少特征基因，并用一个性能较好的分类器进行分类预测。针对该问题，用修订的特征记分准则（RFSC）去除分类无关基因；对两两冗余法进行改进，提出强相关树法用于冗余基因的去除；对粗糙支持向量机（RSVM）改进，提出近似等价粗糙支持向量机（AE-RSVM）对样本集进行分类测试。以肿瘤样本集为例进行测试，实验结果表明了提出方法的可行性和有效性。
　　대종류기인표체보진행분석，종이유효구분정상양본여종류양본적관건시：준학조출능구결정양본유별적최소특정기인，병용일개성능교호적분류기진행분류예측。침대해문제，용수정적특정기분준칙（RFSC）거제분류무관기인；대량량용여법진행개진，제출강상관수법용우용여기인적거제；대조조지지향량궤（RSVM）개진，제출근사등개조조지지향량궤（AE-RSVM）대양본집진행분류측시。이종류양본집위례진행측시，실험결과표명료제출방법적가행성화유효성。
The key of distinguishing between normal and tumor samples effectively for tumor gene expression data is to find out the fewest genes which can predict the classes, then use a good performance classifier to classify. Faced with the problem, it uses the Revised Feature Score Criterion(RFSC)to remove the genes irrelevant to the classification task. It improves the pair-wise redundancy method, proposes strong correlative tree to filter the redundant gene. It improves the Rough Support Vector Machine (RSVM)and proposes the Approximate Equivalence Rough Support Vector Machine(AE-RSVM), and then validates classifica-tion for data sets. Using the tumor data set to test, the experimental results show the feasibility and effectiveness of the method proposed in this paper.