计算机与现代化Issue(3)：11-15,5.DOI:10.3969/j.issn.1006-2475.2016.03.003
一种基于旋转森林的甲状腺疾病分类方法
A Classification Method of Thyroid Disease Based on Rotation Forest
摘要
Abstract
Thyroid disease is common in the field of endocrine, accurate identification of different types of thyroid disease is the primary problem of clinical treatment.By using the results of clinical experiments, this paper presents a new method for thyroid disease classification.The method uses principal component analysis to reduce data dimension, and then implements classification task based on rotation forest algorithm.Rotation forest algorithm can make the difference between the base classifiers more obvi-ous, and then improve the accuracy of the classifier, and it can reduce the processing time at the same time.Experimental results show that the classification accuracy of this method can reach to 96.28%on the dataset from UCI machine learning repository.In order to verify the effectiveness of the method furthermore, this paper also chooses the real clinical medical data set, it is more complex than the UCI standard dataset in data quantity and data dimension.Compared with the other method, the classification accuracy of this method reaches to 96.37%.关键词
甲状腺疾病/集成分类/旋转森林/特征选择/主成分分析Key words
thyroid disease/ensemble classification/rotation forest/feature selection/principal component analysis分类
信息技术与安全科学引用本文复制引用
潘乔,许腾,陈德华,徐光伟..一种基于旋转森林的甲状腺疾病分类方法[J].计算机与现代化,2016,(3):11-15,5.基金项目
上海市自然科学基金资助项目(15ZR1400900) （15ZR1400900）
上海市科委科技创新行动计划项目(13511504905) （13511504905）
