CAJ | 학술논문

支持向量机是人工智能研究领域中的重要课题,但该算法不能够对复杂高维的生物医学数据进行准确的分类,而FSVM方法能够利用模糊性对标记样本数据进行较准确的归类,故采用FSVM算法对老年痴呆数据进行分析。通过特征提取方法对数据进行降维,采用主成分分析法提取出数据的11个主成分,并筛选前3个主成分和前2个主成分分别进行分类模型的训练。利用基于FSVM的模糊C均值聚类方法将老年痴呆的121个样本分成了正负两个类别,实验结果表明,FSVM算法能够有效地分析老年痴呆数据。
지지향량궤시인공지능연구영역중적중요과제,단해산법불능구대복잡고유적생물의학수거진행준학적분류,이FSVM방법능구이용모호성대표기양본수거진행교준학적귀류,고채용FSVM산법대노년치태수거진행분석。통과특정제취방법대수거진행강유,채용주성분분석법제취출수거적11개주성분,병사선전3개주성분화전2개주성분분별진행분류모형적훈련。이용기우FSVM적모호C균치취류방법장노년치태적121개양본분성료정부량개유별,실험결과표명,FSVM산법능구유효지분석노년치태수거。
Support vector machine is an important subject in the field of artificial intelligence. But the algorithm is not able to classify the biomedical data in complex high-dimensional accurately. However, fuzzy support vector method can classify labeled sample data accurately using the fuzziness. Therefore, fuzzy support vector machine is used for Alzheimer’ s data analysis. In the process of experiment, the feature extraction method is used to re-duce the data dimension and the principal component analysis method is adopted to extract the eleven principal components of the data. Finally, three principal components and two principal components are screened to train the classification model. Based on the model and the fuzzy C-means clustering method, one hundred and twenty one samples of Alzheimer’ s data is divided into two categories of postive and negative categories. The experimen-tal evidence suggests that the fuzzy support vector algorithm analyze the Alzheimer’ s data effectively.